{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Focusing Properties of a Metasurface Lens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example demonstrates how to compute the far-field profile at the focal length of a metasurface lens. The lens design, which is also part of the tutorial, is based on a supercell of binary-grating unit cells. For a review of the binary-grating geometry as well as a demonstration of computing its phasemap, see [Tutorial/Mode Decomposition/Phase Map of a Subwavelength Binary Grating](https://meep.readthedocs.io/en/latest/Python_Tutorials/Mode_Decomposition/#phase-map-of-a-subwavelength-binary-grating). The far-field calculation of the lens contains two separate components: (1) compute the phasemap of the unit cell as a function of a single geometric parameter, the duty cycle, while keeping its height and periodicity fixed (1.8 and 0.3 μm), and (2) form the supercell lens by tuning the local phase of each of a variable number of unit cells according to the quadratic formula for planar wavefront focusing. The design wavelength is 0.5 μm and the focal length is 0.2 mm. The input source is an $E_z$-polarized planewave at normal incidence.\n",
    "\n",
    "The key to the script is the function `grating` with three geometric input arguments (periodicity, height, and list of duty cycles) which performs the two main tasks: (1) for a unit cell, it computes the phase (as well as the transmittance) and then translates this value from the range of [-π,π] of Mode Decomposition to [-2π,0] in order to be consistent with the analytic formula for the local phase and (2) for a supercell, it computes the far-field intensity profile around the focal length of the lens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import meep as mp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "resolution = 50  # pixels/μm\n",
    "\n",
    "dpml = 1.0  # PML thickness\n",
    "dsub = 2.0  # substrate thickness\n",
    "dpad = 2.0  # padding between grating and PML\n",
    "\n",
    "lcen = 0.5  # center wavelength\n",
    "fcen = 1 / lcen  # center frequency\n",
    "df = 0.2 * fcen  # frequency width\n",
    "\n",
    "focal_length = 200  # focal length of metalens\n",
    "spot_length = 100  # far field line length\n",
    "ff_res = 10  # far field resolution (points/μm)\n",
    "\n",
    "k_point = mp.Vector3(0, 0, 0)\n",
    "\n",
    "glass = mp.Medium(index=1.5)\n",
    "\n",
    "pml_layers = [mp.PML(thickness=dpml, direction=mp.X)]\n",
    "\n",
    "symmetries = [mp.Mirror(mp.Y)]\n",
    "\n",
    "\n",
    "def grating(gp, gh, gdc_list):\n",
    "    sx = dpml + dsub + gh + dpad + dpml\n",
    "    src_pt = mp.Vector3(-0.5 * sx + dpml + 0.5 * dsub)\n",
    "    mon_pt = mp.Vector3(0.5 * sx - dpml - 0.5 * dpad)\n",
    "    geometry = [\n",
    "        mp.Block(\n",
    "            material=glass,\n",
    "            size=mp.Vector3(dpml + dsub, mp.inf, mp.inf),\n",
    "            center=mp.Vector3(-0.5 * sx + 0.5 * (dpml + dsub)),\n",
    "        )\n",
    "    ]\n",
    "\n",
    "    num_cells = len(gdc_list)\n",
    "    if num_cells == 1:\n",
    "        sy = gp\n",
    "        cell_size = mp.Vector3(sx, sy)\n",
    "\n",
    "        sources = [\n",
    "            mp.Source(\n",
    "                mp.GaussianSource(fcen, fwidth=df),\n",
    "                component=mp.Ez,\n",
    "                center=src_pt,\n",
    "                size=mp.Vector3(y=sy),\n",
    "            )\n",
    "        ]\n",
    "\n",
    "        sim = mp.Simulation(\n",
    "            resolution=resolution,\n",
    "            cell_size=cell_size,\n",
    "            boundary_layers=pml_layers,\n",
    "            k_point=k_point,\n",
    "            default_material=glass,\n",
    "            sources=sources,\n",
    "            symmetries=symmetries,\n",
    "        )\n",
    "\n",
    "        flux_obj = sim.add_flux(\n",
    "            fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))\n",
    "        )\n",
    "\n",
    "        sim.run(until_after_sources=50)\n",
    "\n",
    "        input_flux = mp.get_fluxes(flux_obj)\n",
    "\n",
    "        sim.reset_meep()\n",
    "\n",
    "        geometry.append(\n",
    "            mp.Block(\n",
    "                material=glass,\n",
    "                size=mp.Vector3(gh, gdc_list[0] * gp, mp.inf),\n",
    "                center=mp.Vector3(-0.5 * sx + dpml + dsub + 0.5 * gh),\n",
    "            )\n",
    "        )\n",
    "\n",
    "        sim = mp.Simulation(\n",
    "            resolution=resolution,\n",
    "            cell_size=cell_size,\n",
    "            boundary_layers=pml_layers,\n",
    "            geometry=geometry,\n",
    "            k_point=k_point,\n",
    "            sources=sources,\n",
    "            symmetries=symmetries,\n",
    "        )\n",
    "\n",
    "        flux_obj = sim.add_flux(\n",
    "            fcen, 0, 1, mp.FluxRegion(center=mon_pt, size=mp.Vector3(y=sy))\n",
    "        )\n",
    "\n",
    "        sim.run(until_after_sources=200)\n",
    "\n",
    "        freqs = mp.get_eigenmode_freqs(flux_obj)\n",
    "        res = sim.get_eigenmode_coefficients(\n",
    "            flux_obj, [1], eig_parity=mp.ODD_Z + mp.EVEN_Y\n",
    "        )\n",
    "        coeffs = res.alpha\n",
    "\n",
    "        mode_tran = abs(coeffs[0, 0, 0]) ** 2 / input_flux[0]\n",
    "        mode_phase = np.angle(coeffs[0, 0, 0])\n",
    "        if mode_phase > 0:\n",
    "            mode_phase -= 2 * np.pi\n",
    "\n",
    "        return mode_tran, mode_phase\n",
    "\n",
    "    else:\n",
    "        sy = num_cells * gp\n",
    "        cell_size = mp.Vector3(sx, sy)\n",
    "\n",
    "        sources = [\n",
    "            mp.Source(\n",
    "                mp.GaussianSource(fcen, fwidth=df),\n",
    "                component=mp.Ez,\n",
    "                center=src_pt,\n",
    "                size=mp.Vector3(y=sy),\n",
    "            )\n",
    "        ]\n",
    "\n",
    "        for j in range(num_cells):\n",
    "            geometry.append(\n",
    "                mp.Block(\n",
    "                    material=glass,\n",
    "                    size=mp.Vector3(gh, gdc_list[j] * gp, mp.inf),\n",
    "                    center=mp.Vector3(\n",
    "                        -0.5 * sx + dpml + dsub + 0.5 * gh, -0.5 * sy + (j + 0.5) * gp\n",
    "                    ),\n",
    "                )\n",
    "            )\n",
    "\n",
    "        sim = mp.Simulation(\n",
    "            resolution=resolution,\n",
    "            cell_size=cell_size,\n",
    "            boundary_layers=pml_layers,\n",
    "            geometry=geometry,\n",
    "            k_point=k_point,\n",
    "            sources=sources,\n",
    "            symmetries=symmetries,\n",
    "        )\n",
    "\n",
    "        n2f_obj = sim.add_near2far(\n",
    "            fcen, 0, 1, mp.Near2FarRegion(center=mon_pt, size=mp.Vector3(y=sy))\n",
    "        )\n",
    "\n",
    "        sim.run(until_after_sources=100)\n",
    "\n",
    "        return (\n",
    "            abs(\n",
    "                sim.get_farfields(\n",
    "                    n2f_obj,\n",
    "                    ff_res,\n",
    "                    center=mp.Vector3(-0.5 * sx + dpml + dsub + gh + focal_length),\n",
    "                    size=mp.Vector3(spot_length),\n",
    "                )[\"Ez\"]\n",
    "            )\n",
    "            ** 2\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the first of two parts of the calculation, a phasemap of the binary-grating unit cell is generated based on varying the duty cycle from 0.1 to 0.9."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00247622 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00647688 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00136495 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.03,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0103281 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000896931 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.0044961 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00125909 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.0382759,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0100789 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00088501 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00477791 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.0013721 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.0465517,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.00952506 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00219703 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00522304 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00136685 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.0548276,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0103021 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00116801 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00504303 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00156713 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.0631034,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.010608 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00124407 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00682497 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000928879 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.0713793,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.012069 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000815868 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00467396 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000994921 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.0796552,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.00999308 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000801086 s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00456905 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00162697 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.087931,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0103731 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.0009408 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00609112 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00112414 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.0962069,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0113389 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00133109 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.004987 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000950098 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.104483,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.00973797 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00119305 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00534606 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00095892 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.112759,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0165179 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000835896 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00442696 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.001477 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.121034,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0124168 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00136709 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00486684 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000990152 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.12931,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0103049 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.001513 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.005023 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000887156 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.137586,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0100081 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000860929 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.004879 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000782967 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.145862,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0097518 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000844002 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00591779 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00101209 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.154138,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0102739 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000947952 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00474691 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000870943 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.162414,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.00949287 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000854969 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00593901 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00108814 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.17069,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0101011 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000795126 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00502586 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00118899 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.178966,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0093739 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00110197 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00501704 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00141692 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.187241,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.013217 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00104403 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00458407 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000869989 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.195517,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.00961399 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00112414 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.0046351 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000927925 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.203793,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.009727 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00181007 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00469708 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00114608 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.212069,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0103791 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00141215 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00565505 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00124907 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.220345,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.011498 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00082016 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00524902 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000906944 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.228621,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0162721 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00110412 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00537992 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00147796 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.236897,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.010345 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00085187 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00499296 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000822783 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.245172,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0116842 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000720978 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00519085 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000974894 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.253448,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.012033 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 9 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000780106 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00697684 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00137496 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.261724,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0110409 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 7 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000945091 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "time for set_epsilon = 0.00621819 s\n",
      "-----------\n",
      "run 0 finished at t = 75.0 (7500 timesteps)\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000971079 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 0.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,0,0)\n",
      "          size (1.8,0.27,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "time for set_epsilon = 0.0130391 s\n",
      "-----------\n",
      "run 0 finished at t = 225.0 (22500 timesteps)\n",
      "MPB solved for omega_1(2,0,0) = 2 after 8 iters\n",
      "Dominant planewave for band 1: (2.000000,-0.000000,0.000000)\n"
     ]
    }
   ],
   "source": [
    "gp = 0.3  # grating periodicity\n",
    "gh = 1.8  # grating height\n",
    "gdc = np.linspace(0.1, 0.9, 30)  # grating duty cycle\n",
    "\n",
    "mode_tran = np.empty((gdc.size))\n",
    "mode_phase = np.empty((gdc.size))\n",
    "for n in range(gdc.size):\n",
    "    mode_tran[n], mode_phase[n] = grating(gp, gh, [gdc[n]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Z4NVnZsuDYy2W9Kdg+n8hzXROK9bGUiqeLmdmH5bPAVVpoCunUF8l/DdTzd/ckGW4vy1V/9kBiSKHF9CGqrhL+FFFT77XSzpZPpnrBPmn5VjupWheq0q+P2rpCEUr8MO43yefFPT+2Ns7yyf3/LGk58zsZ8Gd2KTbNeSfo5kE0wWv0WCnd638I+Hnyt/57ZZP5Jr73MN/Iw353BXtrG4qWqoyRe/cV4ibQwBQP69UWK43tFzu5ktS5+0rNRjs6pF0jqSj5KfPjZF/Sl3uXBmeMlfwXOmc+1/56XYXKfrzjJCfLvkvkm6V9IiZLSjSprTPaeF2j65h/4r6qGa2t3z/LhcwuV/SP8vn/Zws/7TDcB/10tDuhX7/Y0LLtfzNDUVW+9vS4Gh9KbnfJ1AxOvFAtn0+tHyWc67cfP1Kh/InKrjLeamkS81sD/k7TnPkh7e/NijWKekjkuaa2ZurvPuUNe/RYK6LlZJmO+fWliifxucenhI6pmipyvRq8ALhjRXkbwMANM5oVZYGIDwdqu5pA8zsEElvCR3voDJ5nyo6VwbTwz5iZp+SdIgG+zMHy+elkvyUt2vN7EznXDzfVtrntDXyQTupsjQHtfqsBq9Vl0g6rlQ+VZX//YdvnlUaqEtqCl6W+9vhkXWrE24yUBYjvICMMrPXaDA55cuSvlOm/Dg1wdQu59wTzrmLnHOnOef2kO8UfleDUwr3kPTV1BrYGo4ILZ9bJtglDSbvb6Rwm15jZkO5QROua6+ipQAAaah0umA499SLRUslJ3yuvKSCJOdVnSudc5udc390zn3VOXe4/HTN90gKB7C+ZWbTY7umfU57OrSceEL3kPDv/9/KBLuk8r//cGCmlr+5mtDfjjzM4elkWwmUR8ALaF71nkYXzgHxtwpyJB2qJpyH75x7zDn3BUlfCW0+Nq32JKCWz73afcKffSUJ/OdUWX8S7pW0JVgeLemgIdQVTk571BDqAQAk783lCpjZXhrM3+UUfdJvvTT0XBkEwK6VnxqZe3rjCOWft9I+p4UDcq+r43Eq/v2b2XhJbyhT372h5YPLHTzIuzWUvkdO1vvb4Sd931u0FFAnBLyA5rUltFyPJOjhRxxXMrT7E3VoQ5KuDy1PKVqq+dXyuVe7T8WfvZm9ST5fRkM557Yq+gSuTw2huvDfxgfNbPIQ6gIAJOvkCsqcFlq+zzlXLol3Eqo5V+6shG62BT/b/4U2xfs0aZ/T7got71e01NBV0089XeX7P+E+xWwze33Rkt7hSmYEW2b720HQcGZo013FygL1QsALaF7hxwfHh7Mn4SkNjgyaGczVL8jM3i/pnXVoQ1lmVml+iPDw9FbO31XL517tPk+Glt9drJCZjZb0kwrbUA/hvCUnmNkJNdZzjaTHg+XRkq4ws4qCiWY2xsx4jDYA1M+coJ9RUDC66zOhTT+rf5MkVX6uHCZ/rhxZqjIzm1Tq/ZhSfZq0z2n3aHBK6Z5mVq+bjJX+/vdSBVPrnHMPSbojtOn7ZlbwWtjMuuSn7yUhy/3t/eSfSCpJ9zvn1pQqDNQDAS+geT0QWn5f0pU7517U4LD4DklXmVlkaLqZdZjZJyVdLqlf0ZFEjfJ3M/uJmc0NOpV5zOxgST8MbVrSmKbVRfhzP87MRtRhn/DduVPM7Mz479bM9pR0k6RZSumpOs65P0i6KrTpCjP7ShCIiwj+Vt9mZouCqQ3hevrl75j2B5vmSbrNzIqOXDOzN5jZt+QfXb57sXIAgCHrk3SJmZ0Uf8PMDpS0VIMjYx6TdGGD2rVYg4GKw8zsu2Y2KlzAzKbKB6Dmq/y58gwzu9fMFprZtEIFzGysmX1b0uxgU7+k34fLpH1OC57mF+5nHT6U+koI91W+Z2Z50zfN7AhJy+STvFfSVzkrtHyUpMuCnFnhOidLuk7SG+X/Nock4/3t8JNLry9aCqgjntIINK9rJH08WP6Emc2StFzRRyn/j3PuiSEc48vyQY0OSftLesDMbpe/qzZG/qksuU7ZWZI+psYnMB8l/zjnj0raaGb3ynfYeuWfDvR6Sf8QKv+CpLMb3MYk3SD/GY+WvzP2sJktk7Regx3vm5xzN9W6j3Pu92Z2q/zTd0z+LuYnzWy5pA3yyVUPkTRM0ipJ/60ySVbr6HT5v7kDg/Z8TdK/BH+nz8q3f7r8xUHu7nle7gvn3B/M7BOS/ieo52BJd5nZY/J3q1+W/1ubKt/JZdojADTGF+TPM5eb2dmS/iwfaNhH0RxKr0g6xTnXkGCAc+4RM7tc0inBpjPlpxD+RdLzknaTz9s1Qv4pjl+Q9KMy1e4n6XxJPzSzJyQ9KD9aarh8rqdDFH0y4H86554t0La0z2lXavD3cpykXyRcvySdK/80wJ3kk7jfGPRTHpLv28yS/xuRfFDweZWZHuucu9nMzpV/AqQknSjpWDO7Odh/unwAr0s+wfpvJH06gZ8lq/3tBaHlKxNrKVAFAl5Akwo6M1dIyt3xPEj5yTOvl1RzwMs598fgjtIP5L8PhkuaG7xyBiT9h6RvyZ+AG22TfGdA8nfw3lqi7H2STnDOPVf3VtWJc67HzD4j32nukH8M9GtjxTbJd5xq3kfS++XvzM0K1ndX/l3fhyQdLx9sSkXws82Vvxj6sHzHvlvSkUV22aLBu97xun5qZo9L+rEGn5i0l0o/5WqFpHXVtxwAUAnn3HlmNlE+GfYewStujaQPOOfuKPBePX1CPmiUO+dMU36urpWSTlD5HFIbQ8smac/gVUifpG84575erLKUz2l/lPSMfFBmvpmNcc5tSqDeVznnnjezd0v6rXzARfJ9llmxotfJ53j77wqrPlO+b/s5+c9hrPKnTD4mH8hLZIZFFvvbQV67twSrdwZTSoGGI+AFNLdT5IfUnyh/h25H+btOiXHO/Si4y/RZ+aHHO0vaLD+y52ZJFznn7pEkn3uy4SbJ30E9TD55+l7ySTK75O/2rpT0V/kRcb8Nhtq3tKAT+6D8CL+D5e84jlaJp/ZUu49zbq2ZHSI/guoE+aSio+XvcP5N0q8kXemceyWYUpIa59xmSR8zs+/J/584Qv7O+kT5i4LVku6Xn/byK+fcxiJVyTl3S5CodoH8FJSD5S9mxsn/Pa2V9Ih8wuAbnHM8UQgA6sw5d7aZ3SDpn+QvtHeWtE3+pt4iST90zq1PoV2vmNnRkj4o6VT50Tnj5EdlPSnf97jEOfdycHOmVF3nmNk18lMQD5G0r/y5bJx8sGO9pIfl+16XOeeeqaB9qZzTnHP9ZvYD+RHi3fL9iMRzqznn/mxm+8jncHuXBm/mrZbv+13hnPudVHkf1TnnJH3ezK6StFA+6DRFUo/839uvJV0Y3HBL8mfJWn/7wxpMn1RpMBJInPn/8wAAAABQf2b26gWIcy6Vq3sMTZD76in5m0/3Ouf2T7lJaBJm1ikfPNxFPjD8Oufc9nRbhawiaT0AAAAAoGLOuR5J/xWsvtHMiqUaQPZ8UINPc/wawS6kiRFeAAAAABqGEV7tIXhq5SPywY3bnXOHptwkpCwY3fWgpNdJulfSm9oh3QhaFyO8AAAAAABVCXJsnhmsviVIMo9s+4h8sEuSPkWwC2ljhBcAAACAhmGEFwCgERjhBQAAAAAAgLZCwAsAAAAAAABtpa4BLzObbGbvNLOvm9kNZvaimbngdUmdjnmCmf3ezFab2RYze9rMLjezg6uoY5KZfc3M7jOzDWbWEyx/zcwm1aPdAAAAzczMdjGz75rZw2bWa2brzOwuM/u8mY1Ou31oHc45y73SbgsAoH3VNYdXeH5+AZc6505L8Fhdkq6S9M4iRQYkne2c+/cy9Rwg6TeSphUp8pykdzvn7q61rQAAAK3EzOZLulLS+CJF/ibpGOfck41rFQAAQHGNnNL4rKSb6lj/hRoMdt0i6ThJB8o/KeIJ+Z/162Z2erEKzGy6pN/JB7u2S/qOpDnB6zvBtp0lXR+UBQAAaGtmtp+kX8sHuzZJOkvSIZKOkPTToNjrJC02szGpNBIAACCm3iO8vibpL5L+4pxba2a7SXoqeDuxEV5mdpikZcHq7yQtcM71h97fUdJfJe0i6WVJr3XOrS9QzyWSTg1W3+ecuyr2/vHyHT5Jutg59+Ek2g8AANCszOwWSXPlb/zNcc79Ofb+F+RvDErSV51zX29sCwEAAPLVNeCVd7D6BbwWSzpGUr+k3ZxzKwuUOUHSL4LVzzvnzom9P0XSKknDJP3eOfeOIse6UdJRwbGmO+fWJvEzAAAANJsg1cNdweqPnXMfL1CmQ9KDkvaWv7E4xTm3rXGtBAAAyNfyT2kMhs4fEawuLRTsClwrqSdY/scC7x8rH+ySpItLHPKS4N9hwT4AAADt6rjQcsH+kXNuQNJlweoE+dFgAAAAqWr5gJd8nq6RwfKtxQo55/ok3ZHbx8yGx4q8NbRctJ7Ye4dW2kgAAIAWlOsf9cqnhyiG/hEAAGgqnWk3IAF7h5YfKVP2EUlHyv/ce0l6qEA9G5xza4pV4JxbbWY9ksbFjl2Wmc0oU2SEpNdLel7SC/LTJgEAQHqGSdopWH7AObc1zcakINfXedw5t71EuXAfrOL+EX0jAABaSkv1i9oh4PWa0HKx6Yw5z8b2eyi2XkkduXr2iR27Es+WLwIAAJrUAZLuTrsRjWJmXZJ2DFZL9o+ccy+bWa+kblXXP6JvBABAa2r6flE7TGkcG1reVKZsb2g5/tjsXD3l6gjXw6O3AQBAu6qmjyXRPwIAAE2kHUZ4dYWW+8qUDQ+3G1WknnJ1hOuJ11FOuTue0xXkGbvrrrs0bdq0KqsHAABJWr16tQ488MDc6gtptiUF1fSxpNr6RxX3jZYuXapdd921iqrRCrZu3ao777xTknTQQQdp5MiRZfZAK+JzTl7XW98qW7t2yPW4KVO05U9/SqBFfM5Z8Mwzz2jevHm51abvF7VDwGtLaHlEmbLh/3GbC9QzuoI6wvXE6yipxBMkJUlm9urytGnTNGNGubQWAACggbKWP6qaPpZUQ/+omr7Rrrvuqr322qvSqtEiNm/erCeeeEKStMcee2jUqGrvJ6MV8DnXQWdCl/KdnVJC3618zpnT9P2idpjSuDG0XG4IfXdoOT40P1dPJcPwc/VUMrwfAACgFVXTx5LoHwEAgCbSDgGv8J3BckOiwsPm40lSc/VUMqwqVw+JVgEAQFtyzm2R9GKwWrJ/ZGYTNBjwon8EAABS1w4Br/CTFl9fpmzu/e2SHi9Sz3gzm1qsAjObJmlcsPpwpY0EAABoQbm+zp5mVmr+TLgPRv8IAACkrh0CXn/RYCLVw4oVMrMRkg7O7eOciydf/d/QctF6Yu/dXmkjAQAAWlCuf9Qt6U0lytE/AgAATaXlA17OuY2S/hisvt3Mig25/0cNjsxaVOD930oaCJY/VOKQpwX/DgT7AAAAtKvrQssF+0dm1iHplGB1vaRb6t0oAACAcpo+4GVmp5mZC15nFyn23eDfTknnm9mwWB07Svp2sLpe0s/iFTjn1ki6Mlg9yszeW6Atx0s6Kli9PNgHAACgLTnn7pKUe179R8zszQWKnSlp72D5v51z2xrSOAAAgBISepZpYWZ2qKQ9Q5t2DC3vaWanhcs75y6p5TjOuZvN7JeSTpB0rKSlZvZ9Sc9J2lfSWZJ2CYp/yTn3cpGqzpL0Dkk7SfqFmc2WdH3w3jvlO3SS9IKkL9fSVgAAgBbzaflpiqMk3WRm35QfxTVKvu/1saDco5LOSaWFAAAAMXUNeEk6XdKpRd57S/AKu2QIx/qw/JTFYyS9LXiFDUj6d+fcj4tV4Jx71szeJT98f6qkLwavsDWSjnPOrYzvDwAA0G6cc/eY2fslXSHf1/pmgWKPSpofpJoAAABIXdNPaayUc26zc26+pBMlLZX0vHwy+2cl/VzSoc65syuo5075UWH/IelBSZuC1wPBtplBGQAAgExwzv1O0hsknSsf3HpFPk3E3fI3B/d3zsWfgA0AqJctW9JuAdD06jrCyzl3mgaTvNdaxyWqYuSXc+7n8gGuoRzzRUn/FrwAAAAyzzn3jKTPBS8AQFr+/ndp3bq0WwE0vXpPaQQAAAAAAEno65OOP15yLrp9/HhpzBi/vH691B2gJKYAACAASURBVNs7+F53t7TDDoXrmzq1Pu0EmkDbTGkEAAAAAKCtnXmmdNdd0W3/+I/Syy9LK1f614c+FH3/xBMH34u/7r67cW0HGoyAFwAAAAAAze6Xv5R++MPotj33lC66SDIb3DZyZLTM1q31bxvQhAh4AQAAAADQzB5+WDr99Oi2ri7p6qv9dMYwAl6AJHJ4AQAAAADQOLNnS2vWVF7eOen556Xt26Pbp0yR9tsvvzwBL0ASAS8AAAAAABpnzRpp1aqh1xMPgOWMGBFdJ+CFjCLgBZTQ0+NzOW7a5B96MmOGNG5c2q0CAAAAgCIY4QVIIocXkMc56ZZbpPe+V5o4UdpnH+mgg/y/Eyf6pwDfckv+k4ABAAAAIHUEvABJBLyAiOXLpX33lQ4/XLrmGqm/P/p+f7/PC3n44b7c8uXptBMAAAAACiLgBUgi4AW8aulSac4cacWKysqvWOHLL11a33YBAAAAQMXiAa++vnTaAaSMgBcgP1JrwQKpt7e6/Xp7/X6M9AIAAADQFBjhBUgi4AXIOemUU6oPduX09kqnnkpOLwAAAABNgIAXIImAF6BlyyqfxljMgw9Kt96aSHMAAAAAoHYEvABJBLwAXXBBc9UDAAAAADUj4AVIIuCFjOvpkRYtSqaua6/19QEAAABAagh4AZIIeCHjVq6U+vuTqau/X1q1Kpm6AAAAAKAmBLwASQS8kHGbNiVb38aNydYHAAAAAFWJB7y2bZMGBtJpC5AiAl7ItDFjkq1v7Nhk6wMAAACAqowYkb+tr6/x7QBS1pl2A4A0zZghDRuWzLTGzk5p+vSh1wMAAKo0e7a0Zs3Q65k6Vbr77qHXAwBpio/wkvy0xq6uxrcFSBEBL2TauHHSggXS1VcPva4FC3x9AACgwdasIZEmAOQUC3gBGcOURmTewoXNVQ8AAAAA1IyAFyCJgBeguXOlffYZWh0zZ0qHHZZIcwAAAAC0sw0b6lt/oYAXObyQQUxpROaZSZddJs2ZI/X2Vr9/d7d06aW+HgAAAAAo6qab8h8V39kpTZ5c/QXF1KmFtzPCC5BEwAuQJM2aJS1aJB1zjLR9e+X7dXf7/WbNql/bAAAAALSBtWulU06Jbhs+XLrjjmQvKDo6fBAtfGFDwAsZxJRGILD//tXdVJk5U7rtNmnevPq1CQAAAEAbGBjwwa61a6Pbv/Od+tw9j4/yIuCFDCLgBQR+9jNp27bBdTN/cyTu+OOlW26R7r+fkV0AAAAAKnDOOX46Y9j8+dKnP12f4xHwAgh4AZIf7XvBBdFtJ54oXXNNdNvEidKvf+0T3ZOzCwAAAEBZd90l/eu/RrdNmyZdfHH9LioIeAHk8AIk6be/lZ59NrrtjDOk0aOj29at86PAhg9vXNsAAAAANJHZs6U1ayor65yfxtjfH92+bZt09NHS3Xcn3z6JgBcgAl6AJOkHP4iuH3igf734Yn7ZtWulGTMa0y4AAAAATWbNGmnVqqHV8eKLhZ+mmBQCXgBTGoEHHpCWLYtuO+MM/++kSfmjuVavbkizAAAAAKA2BLwAAl5AfHTX5Mk+Mb3kp9RPnRp9n4AXAAAAgKY2YkR0nYAXMoiAFzJt3Trpiiui2/7pn6I3RKZNi75f6XR9AAAAAEgFI7wAAl7ItosukjZvHlzv7JQ+/vFoGUZ4AQAAAGgpBLwAAl7Irv5+6fzzo9ve8x5p552j2+IjvAh4AQAAAGhq8YBXX1867QBSRMALmbV4sfT009FtuWT1YQS8AAAAALQURngBBLyQXfFk9fvvLx1ySH65+JRGcngBAAAAaGoEvAB1pt0AIA0PPyz94Q/RbWec4Z/KGMcILwAAmlz47tTq1dLAQOFyHR35J/Zi9QBAKyPgBRDwQjb98IfR9UmTpBNOKFy20FManSscHAMAACm4+27/7/XXS+96V/Fyo0dLK1c2pk0AkCYCXgBTGpE9GzZIl14a3fbRj0qjRhUuHw94bdsmrVtXn7YBAIAhuOyy6PoOO0TXueADkBUEvAACXsieSy6RensH1zs6pE98onj5yZPztzGtEQCAJrN+vfTb30a3nXRSdH3btuLTHQGgnRDwAgh4IVsGBvKnMy5YIO2yS/F9RoyQdtwxuo2AFwAATebqq6MXdMOHS6eckl+ur69xbQKAtBDwAgh4IVt+/3vp8cej2844o/x+JK4HAKDJXX55dH3+/MIJ6rnoA5AFBLwAAl7IlvPOi67vu680Z075/QolrgcAAE3i6ael226Lbjv5ZKmrK7/sli0NaRIApGrEiOg6AS9kEE9pRGY8+qh0443RbWecUdnTFuNPKWeEFwAATeSKK6LrEyb4EV6Fpi9y0QcgCxjhBRDwQnacf350fcIE6cQTK9uXKY0AADQp5/KnM77//f5ir9BdLS76AAxVf3/aLSiPgBdAwAvtq6dHWrlS2rTJ93cvuij6/kc+Io0eXVldTGkEAKBJ3XWXH8YddvLJ/t/hw/PLc9EHYKgKfY/stFP+NMJy4tNIkhQPePHADmQQAS+0FeekZcv8aK7rrit982XhwsrrZUojAABNKj66a489pDe/2S+b+Yu+8MUpObwADMUf/iC9/HJ02+mnSz/9aTrtKYYRXgABL7SP5cv908dXrChfduxYf57afffK6mZKIwAATaivT/rlL6PbTj45OpWxqyt6ocdFH4Ba9fVJn/pUdNvEidK3vpVOe0oh4AXwlEa0h6VL/dMWKwl2SdLGjb780qWVlY8HvDZulHp7q2sjAABI2I03Si+9FN120knRdS76ACTl3HOlv/0tuu1b35J23DGd9pTCdx9AwAutb/lyacGC6gNQvb1+v+XLy5eNB7wk8ngBAJC6yy6Lrr/lLX5KYxgXfQCS8Oyz0te/Ht12wAE+MXAz4rsPIOCF1uacn8ZY62ir3l7p1FN9PaWMGSN1d0e3Ma0RAIAUvfyy9LvfRbflktWHcdEHIAmf+5z0yiuD62Y+cfCwYem1qRS++wACXmhty5ZVPo2xmAcflG69tXw58ngBANBErroq+tSxESOk970vv1z8oo+k9QCqddNN0tVXR7d97GN+hFezIuAFEPBCa7vggsbVQ8ALAIAmEp/O+K53SRMm5Jfr6oquc9EHoBpbt+Ynqp80SfrGN9JpT6UIeAEEvNC6enqkRYuSqevaa319pUydGl0nhxcAACl58knp9tuj2wpNZ5S46AMwNOecIz32WHTbf/6nD3o1s/h337Zt0sBAOm0BUkLACy1r5Uqpvz+Zuvr7pVWrSpdhhBcAAE3iiiui65MmSUcfXbgsAS8AtXrmGek//iO67aCDpA9/OJ32VCP+3SdFp4EDGdCZdgOAWm3alGx9GzeWfp+AF3J6enzAddMm/0CDGTOkcePSbhUAZIRz0uWXR7e9//0+h1chBLwAhM2eXflUjZdeys/799RT0i67+Okfd9+dfPuSUug7cevW/GneQBsj4IWWNWZMsvWNHVv6/XjAiymN2eKcf0jC+edL110XHV04bJi0YIG0cKE0d65/aA8AoE7uuEN6/PHotlNOKV6epPUAwtasKT+1o5Tnn0+uLfVUaIQXAX9kDFMa0bJmzEjuKcCdndL06aXLxHN4McIrO5Yvl/bdVzr8cOmaa/Kn0vb3+wf3HH64L7d8eTrtBIBMiI/u2msv6cADi5cnaT2ALCLgBRDwQusaN86PqknCggXlp6TFR3i98IK0fXsyx0fzWrpUmjNHWrGisvIrVvjyS5fWt10AkElbt0q/+lV028knlx5ay5RGAFlEDi+AgBda28KFjasnHvByTlq7NpnjozktX+6Dob291e3X2+v3Y6QXACRsyRJp3brotpNOKr0PAS8AWcQIL4CAF1rb3LnSPvsMrY6ZM6XDDitfbtIkP/UxjDxe7cs5nxKm2mBXTm+vdOqpvh4AQELi0xnf+lZp991L70PAC0AWdXTkX7zw/YeMIeCFlmYmXXaZ1N1d2/7d3dKll1aWZLyjQ5oyJbqNPF7ta9myyqcxFvPgg9KttybSHADA+vXS9ddHt518cvn9SFoPIKsI+CPjCHih5c2aJS1aVH3Qq7vb7zdrVuX7xKc1EvBqXxdc0Fz1AEDWDbvhBmnbtsENI0dKxx9ffkeS1gPIKgJeyDgCXmgL8+ZJt91W+fTGmTN9+XnzqjtOPODFlMb21NPjg6FJuPZaXx8AYGiGxb+Yjz1W2mGH8jtywQcgq/j+Q8YR8ELbmDVLeuAB6ZZbpIkT89/v7PQ3gm+5Rbr//upGduVMnRpdZ4RXe1q5UurvT6au/n5p1apk6gKALBt2773RDTffLM2YMfiaPbvwjlzwAcgqvv+QcZ3liwCtw8wnsp84MfoQp3PPlT78YWncuKHVz5TGbNi0Kdn6Nm5Mtj4AgKSXXqqsHBd8ALKK7z9kHCO80JZefDG6vv/+Qw92SQS8smLMmGTrGzs22foAAFUgaT2ArCLghYwj4IW2s22bf5BT2I47JlM3ObyyYcYMadiwZOrq7JSmT0+mLgBADUhaDyCrRoyIrvP9h4wh4IW2Ex/dJUk77ZRM3YVyeDmXTN1oHuPGSQsWJFPXggXJjC4EANSIEQ4AsorvP2QcAS+0nXjAy6xwEvtaxEd49fVJL7+cTN1oLgsXNlc9AIAaccEHIKv4/kPGEfBC23nhhej6hAl+WlkSpkzJ30Yer/YU/zuqxcyZ0mGHDb0eAMAQcMEHIKvi3399fem0A0gJAS+0nfgIr6SmM0r+nBEfLUYer/bzm99IJ544tDq6u6VLL/UjDAEAKSJpPYCsIuCPjEto3AvQPOIjc5JKWJ8zbZq0bt3gOiO8WktPj7RypbRpk38a44wZ0RxbN9wgHX+8tH177cfo7pYWLZJmzRp6ewEAQ0TSegBhWUrAS8ALGUfAC20nHvBKcoSX5ANeK1YMrhPwan7OScuWSeefL113ndTfP/jesGE+sfzChT7ItWCBf9Jn2PHHSw89FP3ci5k504/sItgFAE2CCz4AYYXuak6cKI0aVV098adZNSO+/5BxBLzQduo5pVHKT1zPlMbmtny5dMopxYNV/f3S1Vf7l1n+Tb/TT5d+/GP/3q23+qDZokXRoFnOuHHSPfcklzMOAJAALvgA5DzwgPTSS9Ft73iHtGRJe+ah4PsPGUcOL7Sdek9pjN/MYYRX81q6VJozp7KRWVJ+sOvkk32wq6PD94HmzpWuuspPaX3oIR/4Cuvpkf7610SaDgBISvyCb2BgaPPWAbQm56RPfjJ613LkSOkHP2jPYJdEwAuZR8ALbafRI7wIeDWn5cv99MTe3tr2nzdPuugiH+yKGzdO2ntv6bjj/L9hixfXdjwAQJ3EL/gkEtcDWXT55dKf/hTd9sUvSnvumU57GoGAFzKOgBfaTiOS1ocR8Go+zvlpjLUGuyTpued8fq9yjjkmur5kSe3HBADUQTxpvcRFH5A169dLX/hCdNvuu0tf+lI67WkUAl7IOAJeaDuNSFofRg6v5rNsWeXTGItZscLn7Cpn/vzo+l//ShAUAJpKoRFeXPQB2fLlL0vPPx/d9oMfVJ+ovtUQ8ELGEfBCW3Gu/lMa4zm8NmyQNm9O9hgYmgsuaFw9b3mLNHZsdNuNNyZzfABAAgh4Adm2fLn0P/8T3fbud+fftWxHI0ZE1/nuQ8YQ8EJb2bAhPw9tvac0SozoaSY9PfnJ5Gt17bW+vlJGjJCOPDK6jWmNANqBmY0xszlm9nkz+7WZPWVmLng9nXb7KkbAC8iugQFp4UL/b86oUdL3v59emxqJEV7IOAJeaCvx0V1S8iO8xo6VRo+ObmNaY/NYuTL68J2h6O+XVq0qXy6ex+umm6Rt25JpAwCk6HeSbpX0X5KOl7Rbqq2p1bBh+UkZSVoPZMOFF0p33hnddtZZ0m67pdKchiPghYzrTLsBQJLi+btGjcoPTg2VmZ/W+OSTg9sY4dU8Nm1Ktr6NG8uXOfro6HpPj3T77dLcucm2BQAazELLL0u6W9KbJY1JozFuyhSps0TXNZ5zIKyrK/okEy76gNY0e3bld5oHBvLLdnb66Y2LFkl33518+5pNPODV15dOO4CUEPBCW6l3wvqcadMIeDWrMQlfhsXzcxUybZo0a5ZPEZGzeDEBLwAt7+eSfiLpLufc45IUTGVMJeC15U9/kvbaq7adR44k4AW0gzVrKht+X8z27UPbv9UwwgsZx5RGtJV6J6zPiefxIuDVPGbMyJ+5UqvOTmn69MrKxqc1kscLQKtzzv3EOffzXLCrpXHRByCL+O5DxhHwQluJj/BKOmF9TjzgRQ6v5jFunLRgQTJ1LVjg66tE/EE/Dz0kPf10Mu0AAAwRF30AsojvPmQcAS+0lUaN8IqnCWGEV3NZuLDx9RxwQH6AlVFeANAk4hd9JK0HkAUEvJBxBLzQVtIa4UXAq7nMnSvts8/Q6pg5UzrssMrLDxsmveMd0W0EvACgSXR1Rde56AOQBQS8kHEkrUdbaWTS+jACXs3FTLrsMmnOnGiO4kp1d0uXXurrqcYxx0hXXDG4fvPN0ubN/mmhAIB8ZjajTJFXx1Rv3bpVmzdvruk4I4cPj9zl7du4Uf011oVkbQmNttvCyLu2ldTn3OWcquyeFeSc05YMfAd0SAqHvNzWrXX9ufn/3P62tljQlIAX2kpaUxpfeEHq708uWTqGbtYs/8TpBQuqC3p1d/v9Zs2q/phHHSV1dPinYEs+2LVsmXT00dXXBQAZ8WylBe+880498cQTNR3kLa+8ovCg74fvvVdPx+9eIXW33XZb2k1AAwzlcz5y61YlcR9xy9atuummmxKoqbmNf+IJzQ2tuy1bGvZz8/+5Pb0Yv+BuckxpRFtJa0rjwID0/PP1ORZqN2+edNttlU9vnDnTl583r7bjTZwovfnN0W1MawSA9A0MHx5Z79i2LaWWAEDj5H33bd8+eGcWyABGeKGtNGqE1047+dFc/f2D21avzg+EIX2zZkkPPOBHWr397fnn+M5OPwps4UKfs6vaaYxxxxwj3X774PrixdJ55w29XgAoxMw6JSURvfmQc+6SBOqp1mvKvD9V0l8k6aCDDtIee+xR00FG/OhH0n33vbr++t12055HHllTXUjWli1bXh0JMmfOHHXF862hLST1OXfFc1LVqGvkSB2Zge8AKzAq9si5c/PzGiaE/8/tr9aR1mkh4IW2sXWrtHFjdFu9Al4dHdKUKdJzzw1uW7OmPsfC0JlJ++6bH+z6wx/80xXHjUvuWPPnS2edNbj+1FPS3/4mvf71yR0DANqFc25lqfctdLdg5MiRGlVrUsTu7sjq8IEBDSfBYtPp6uqq/TNGyxjS55zQHUQzy8bfWoFO7qiOjoYkmOX/c3samVDQuVEIeKFtxKczSvWb0ij5PF7hgBeJ65vbs7EsMR0dfkRXZ8Lfgm94gzR9urRq1eC2xYsJeAGoD+fcdjPbO4Gq2vssxpPKAGRRoeAE33/IEAJeaBvx6YwdHdKECfU7Hk9qbC1//3t0feedkw92Sf7G4zHHSD/96eC2JUukM89M/lgAIEnOuUfSbkPTI+AFIIsKBbz6+hrfDiAlJK1H24iP8Jo0yQe96oWAV2uJj/DaZZf6HeuYY6Lrt90m9fTU73gAgDLiF31btqTTDgBoJEZ4IeMIeKFtxANe9crflTN1anSdHF7NLT7C6zXl0iQPwdvfLoUfirN9u88XBgBICSO8AGQRAS9kHAEvtI1GPaExhxFerSUe8KrnCK8xY3x+sLAlS+p3PABAGfEnhXHBByALOjryc3jw/YcMIYcX2kZ8hFc9E9ZLBLxaTSOnNEp+WmN4VNeSJZJziT1cCADqzsz2lHRobPOY3L9mdlrsvRudc8053pkRXgCyauRIP90gh+8/ZAgBL7SNtEd4rVlDQKOZNXJKoyTNny997nOD66tXS/feK+2/f32PCwAJOlTSxUXem1TgvbdJIuAFAM1k5Eipt3dwne8/ZAgBL7SNRo/wiufw2rJF2rBB2mGH+h4X1du+XXruuei2eo/w2msvaY89pCeeGNy2eDEBLwBIBUnrgfYwebK/y9zfP7ito8N3zKu56xzvyLczAv7IMAJeaBtpJ62X/CgeAl7N57nnpIGB6LZ6B7zM/Civ884b3LZkifTlL9f3uACQFOfcJZIuSbkZyeCCD2gPRx0l3XNPdNsVV0gf+EA67WkFfP8hw0haj7bR6CmNXV3ShAnRbeTxak7x6YyjRkkTJ9b/uMccE12/4478v1MAQAOQtB5ofQ89JH33u9Ftb3ubdMIJ6bSnVRDwQoYR8ELbaPSURqlwHi80n0IJ6xuRa+2ww6TRowfXnZN+//v6HxcAEMMFH9DanJM++clo8vXhw6XzzyeBbjl8/yHDCHihLQwMSC+9FN1W7xFeUv60RkZ4NadGJ6zP6eqSjjgium3JksYcGwAQwgUf0Np+8Qtp2bLots9/Xtp771Sa01JGjIiu8/2HDCHghbbw8sv5OZrSGOFFwKs5xQNe9c7fFRaf1njjjdE8qwCABiBpPdC6NmyIPvpa8p25s85Kpz2thoA/MoyAF9pCfDqj1JgRXgS8WkOhKY2NEg94rVsn3Xln444PABAXfEAr+7d/k9aujW477zypuzud9rSa+PdfX1867QBSQMALbSGeCHzs2Pzv9nqIT2kkh1dzSmtKo+SDazNnRrcxrREAGoyk9UBruucen6cr7J3vlI49Np32tCIC/sgwAl5oC2kkrJcY4dUq0hzhJUnz50fXFy9u7PEBIPO44ANaz8CA9IlPRPOWdHX50V0kqq8c33/IMAJeaAvxEV6NmM4oEfBqBZs2+WmEYY0c4SXlT2u8915p1arGtgEAMo0LPqD1XHhhfh6Is86Sdt89nfa0Kr7/kGGdaTcASEKzjPBav97nwY3PnEB64qO7pMYHvA45RBo/3udczbnhBun00xvbDgDIrEI5bAYGpA7u/QKNctiZZ2rk+vXqGjmy/AitgYH8XCGdndKPfiRdd5109931a2i7IeCFDCPghbYQD3g1aoRXPIeX5M/Nu+3WmOOjvHjAa8cdpdGjG9uGzk7pqKOkX/96cNuSJQS8AKBhCiX27OvjDhXQQCPXr9eol16qvYLt2xkiXwsCXsgwbmuhLaQ1pXH8+Py+MtMam0uaCevD4tMaly6lvwEADVMosMWXMIAsIOCFDCPghbaQ1pRGM/J4Nbt4wKvRCetzjj46ur5pk3TlldJdd0kPPST19KTTLgDIhEIjvLjoA5AFBLyQYQS80BbSGuEl5U9rjKcbQLriUxrTGuE1ebI0e3Z020c+Ih10kLTPPtLEidLxx0u33CI5l04bAaBtEfACkFUEvJBhBLzQFtIa4SUxwqvZNcsIr+XLpWeeKf5+f7909dXS4YdL++7rywMAElIo4LVlS+PbAQCNRsALGUbAC20hraT1EgGvZhcf4ZVGwGvpUmnOnPy/02JWrPDlly6tb7sAIDNGjMjfxkUfgCwg4IUMI+CFlvfKK9LmzdFtaQa8mNLYPJxLP2n98uXSggVSb291+/X2+v0Y6QUACTDjog9ANsUD/nz3IUMIeKHlFRo108gpjfEcXozwah4vvJB/Tm/kCC/npFNOqT7YldPbK516Kjm9ACARBLwAZFH8u6+vL512ACkg4IWWF09Y39kpjR/fuOMzpbF5xaczDhuW/3nV07JlfnriUDz4oHTrrYk0BwCyjYAXgCziuw8ZRsALLa9Qwnqzxh0/HkBZu9YnIEf64tMZp0/3Qa9GueCC5qoHADItftFH0noAWUDACxlGwAstL82E9VJ+wGtgIH/UGdKR5hMae3qkRYuSqevaa319AIAh4KIPQBbx3YcMI+CFlhcPLjU64LXTTlJH7H8S0xqbQ3xKYyMT1q9cmdxIv/5+adWqZOoCgMzq6oquc9EHIAsIeCHDCHih5RWa0thIw4ZJkydHtxHwag5pjvDatCnZ+jZuTLY+AMgcLvoAZBHffcgwAl5oeWmP8JJIXN+s4iO8GhnwGjMm2frGjk22PgDIHC76AGQR333IMAJeaHlpj/CSpKlTo+tr1jS+DcgXH+HVyCmNM2YklyC/s9Mn3AcADAFJ6wFkEQEvZBgBL7S8tJPWS4zwakZ9ffmfQyNHeI0bJy1YkExdCxb4+gAAQ8BFH4As4rsPGUbACy2PKY0o5LnnJOei2xo5wkuSFi5srnoAINNIWg8gi+IBr23b/GPlgQzoTLsBwFA1w5TGeMCLKY3pi09n7O6WJkxobBvmzpX22UdasaL2OmbOlA47LLEmAUB2McoBSFXf2LHqWrdOFr4jOWyYNGWKZFZ5RfFcIigt/t0n+akQ8ZsAQBsi4IWWtn27tG5ddFsaI7zi511GeKWvUML6avpSSTCTLrtMmjNH6u2tfv/ubunSSxvfbgBoSwS8gFSt32MPjX/66ejG66+X3vGOVNqTGSNG5G/bupWAFzKBKY1oafFgl9QcI7xWr86fTofGSjNhfdisWdKiRT54VY3ubr/frFn1aRcAZA5J64HUdPz5z9r1j3+Mbnzvewl2NUKxEV5ABhDwQkuLT2eUmiPgtXmz1NPT+HZgUDzg1ciE9XHz5km33eanN1Zi5kxfft68+rYLADKFEV5AOrZt0/B//ufotjFjpO9/P532ZE2hgBfff8gIAl5oafGE9TvsIA0f3vh2FEolQB6vdMWnNKY1witn1izpgQekW27xNzSHDcsvM2+ef//++xnZBQCJI2k9kI7zzlNHPKHp178uTZ+eTnuyhoAXMoyAF1paMySsl6RRo6Tx46PbyOOVrmYa4ZVj5hPZX3WVn44bD5R+/OP+fXJ2AUAdMMILaLxnn5W++tXIpoF995XOOCOlBmUQAS9kGAEvtLR4wCuNhPU5hfJ4IT3NGPAKGzdOet3rotuefDKdtgBAJhDwAhrvM5/Je3LPtvPOkzp5dlrDdHTk/775/kNGEPBCDtSI7gAAIABJREFUS4tPaUxrhJeUH/BiSmN6enqkDRui29Ke0ljIHntE1594Ip12AEAmkLQeaKwlS6Rrr41senrePA0cdFBKDcowAv7IKAJeaGnNNMIrPj2NEV7piefvkqQZMxrfjnJe+9roOgEvAKgjLviAxnnlFelTn4ps2jpunB465ZSUGpRxfP8hoxhLipYWH+HFlEZI+QGvnXbyedaaTXyEF1MaAaCOSFoPDM3s2ZVPYejpkTZujGyy/n697TOf0cjvfU9avrwODURRBLyQUQ0LeJnZLpL+WdJ8SbtI2irpcUm/lnSBc+6VBI7xBkmfkjRX0nRJ/ZKelbRY0g+dc38vvverdQyTdIKkD0p6k6SJktZJeljSLyRd5JzbPtS2IhnNkrReIuDVTJo9f1dOPOD1zDPS9u2ktQCAuuCCDxiaNWukVatq3n1Eb6/U2ytXKIk66ovvP2RUQy6rzGy+pCslhZ9jN1rSAcHrdDM7xjlX8/gGM/uapH+TFH++2T8Er38ys9Occ4tK1DFN0rWSDo69NSV4zZX0UTN7p3Nuba1tRXKaaUojObyaR6sEvOJTGrdv96PTdt89nfYAQFvjgg9AVvH9h4yqew4vM9tPfhTXeEmbJJ0l6RBJR0j6aVDsdZIWm9mYGo/xJUlfkQ92rZb0Ofmg1cHB8hpJ4yT9wswOKVLHKPmRYLlg1x8kvVd+lNeRkn4iaUDSbEnXmxm3JppAMyWtJ4dX84hPaWzGhPWSNHGiNH58dBt5vACgTkhaDyCrCHghoxqRtP778qO5tks60jn3Tefcn51zNzvnPibpX4Jyr5cPTlXFzKZLOjtYfU7SbOfcuc65O4PXufJBquckjZR0gZkV+rk/KWn/YPnioK3XOOeWO+eWOuf+SdLpwfuzg/JIkXPNPcJr3TrOJWlplRFeZiSuB4CGIYcXgKwaMSK6zvcfMqKuAS8zO0B+GqAkXeic+3OBYufI58eSpM+Y2fAqD3OCfCBLkr7qnHsuXsA5t0rSV4PV/SQdXaCeU4N/eyV91jnnCtRzsaTbg9V/KRI4Q4Ns2iT19UW3NVPAS2JaY1paZYSXROJ6AGgYRjgAyKr491/8IgpoU/UO2BwXWr64UAHn3ICky4LVCRoMkFXqgNDyDSXK3Rhafm/4jWA648xg9c/OuQ0V1DNF0lsrbSSSFx/dJaU7pXGHHfLPJQS8Gm9gID/g1awjvKT8gBcjvACgTgh4Acgqvv+QUfUOeOUCQr2S/lqi3K2h5UOrPMbE0HKpRPLh9w6LvTehwjrK1YMGige8Ro6UxtSUBS4ZZuTxagbPP59/06qZA17xKY2M8AKAOiGHF4CsIuCFjKp3wGvv4N/HnXPbS5R7pMA+leoNLY8vWir63m5mNrqGOuLv/0OZsqijQgnrLf6MzgaLT2sk4NV48dFdnZ3SlCnptKUShUZ45U+oBgAMGRd8ALKK7z9kVN0CXmbWJSk3wWxlqbLOuZc1GHSqNtvOw6HlUiOu5oSbJ2lG6Pgb5J/uKEkHm1ksq1/ReqoaN2JmM0q9JE0tWwle1UwJ63PiAS+mNDZePGH9jBnSsGHptKUS8RFePT3SSy+l0xYAaGvxpPUDA9L2UvdjAaBNEPBCRtVzhNfY0PKmCsrnAl7VTkr7TWj5K0GgLSLY9pXY5rGx9Vw9O0o6s9CBzOxQSfNL1FHOs2Vef6myvkyLj/BqhoAXUxrTFw94NXPCesm3r7Mzuo1pjQBQB/ELPomLPgDZQMALGVXPgFc48FTJYyBy/+tGVXMQ59ydkn4brO4n6VYzO8LMRgevI+RzhO0Xa0f8OP8pqSdY/oaZnWtme5nZcDObamaflLREfnTY9iJ1oIHiI7zSTFifw5TG9LVSwnrJB7t22y26jcT1AFAHBLwAZBUBL2RUPQNe4UygpaYI5uT+F26u4VinSrojWD5Q0h/kR4z1BssHyo+eujK0z8ZwBc65ZyQdH2w3SZ+R9Kh8kGy1pB/Kj+j6kgZ/tkgdFXhNmdcBxXdFXCtMaSTg1XjxEV7NHvCSSFwPAA1RKOBF4noAWUDACxlVz4BXOBhUyTTF7uDfSqY/Rjjn1svn7/qspIdib6+R9O/yT4wcF9r+coF6bpL0RkkXSopn0blD0jGSztfgz5NXR5l2riz1CtqKCrXClEZyeDVefIRXs09plAonrgcAJIwRXgCyioAXMqqzfJHaOOe2mNmL8jmxZpQqa2YTNBjwerZU2RLH65P0fUnfN7PxkibLB8/WOOefeWZmbwiKb1SRRPrOuSclnW5mH5VPIj82qKMnqOOQUPF4cA0N1ApTGteu9TlxO+r9PFS8qh1GeBHwAoA6IOAFIKsIeCGj6n0ZnnuC4p5mViq49voC+9TMObfBOfeYc251KNg1RdKeQZG/OOcGytThgv0fzQW7AoeGlu8caltRu2Yc4RUPeG3fnt9O1M/Wrfmj6loh4BUf4cWURgCog87O/Mf2ctEHIAsIeCGj6h3w+t/g325JbypR7rDQ8u11assH5HNzSdKvh1DPB4N/N0v63ZBahCFpxhFekydLZtFtTGtsnFWr8re14pTGVatIKwMAdcFFH4As4rsPGVXvgNd1oeUPFSpgZh2STglW10u6JelGmNk4SV8MHePnNdZzvPzTHiXp50HuMKSgr0/asCG6rRlGeHV25reDxPWNE5/OOHasNH58Om2pxu67R9edk55+OpWmAEB7i1/0cXcBQBaMiD1Drq8vnXYADVbXgJdz7i5JfwpWP2Jmby5Q7ExJewfL/+2c2xZ+08xOMzMXvM4udBwzm2Zmw4u8N1bSNfL5uCTp8865gk9XNLOik5/M7CBJPwtW10n612JlUX8vxR8poOYIeEk8qTFN8YDXa16TP+KuGY0d60cHhpHHCwDqgFEOQO0mT85PTNvZKU2fXvbldt5ZmydN0uZJk+SmTEmn/VnGdx8yqm5J60M+LT9NcZSkm8zsm/KjuEZJOkHSx4Jyj0o6p8ZjnCjp82Z2qaRbJa2WfyLjwZIWSsoFsi52zl1Yop7FZrZRfgTYfZJ6JU2XdKyk0+R/X1slfdA593yNbUUC4tMZzaSJE9NpS9y0adJ99w2uE/BqnPgTGlshf1fOa18rPR/6ViHgBQB10NUVXeeiD6jce94j3XNPdNtvfysdfXTZXbds3qybbrpJ+v/Zu/c4O+r6/uPv796TzZ1LlmSBcLGVJhHdoqm2JhEN2p8/KtFQL/wgRPxpjdqKUFF/rdWqtbZqvUbRopB6KbekLbZWoiakWkTCokBQkGtJIIEEyCabbPb2/f0xu+yZ7zm7e87ZOec7M9/X8/HYR3bmzJn96obJzns/n89IOuecczStFuvD+Ai8EKiaB17W2juNMW+U9C1FIdTflDjsfkmvHa/yqkzzJb1/5MM1qChMm6wqy0h66chHKbskXWyt/VG1i0Qy3MBr3rziObS+dHTEt5nhVT9ZfELjqNNOk372s7FtBtcDQA1w0wdUZ+9e6W//Nr7vVa+SXvMaP+tBZbj2IVD1qPCStfYmY8wLFFV7vVZSp6R+SQ9Iul7Sl6y1h6fwJTZJapN0tqTTJB2vqBJrl6SbJV1lrd1Zxnkul3SupN+XtEDSHEn7Jf1a0mZJ37DWHprCOpEQ98mHaRhYP4qWRn/cCq8sDKwf5Q6up8ILAGqAmz6gOh/5iHSo4DbIGOnv/z4bsyPAtQ/BqkvgJUnW2kclvW/ko5L3XS3p6kmOeUjSx0c+qmat/U9J/zmVc6A+3AqvtMzvkgi8fMpyhdepp8a3CbwAoAYYWg9U7le/kr7+9fi+iy6SXvhCP+tB5Qi8EKhaP6URqAm3wovAC1LpofVZ4VZ4PfywNDzsZy0AkFvc9AGVu+IKaWhobLutTfr4lOoMUG9c+xAoAi9kklvhlaaWRmZ4+XHggHTQmQKY5Qqvvj7CUgBIHEPrgcps2ybddFN832WXSZ2dXpaDKhF4IVAEXsikLLU09vYWBzFInlvdJWXrZ7ETTpCmOY8sYnA9ACSMmz6gfMPD0uWXx/cdd5z0/lLPCEOqce1DoAi8kElZGlovUalTD+7A+vnzi/9tTzNjmOMFADXHTR9Qvu9+V7rjjvi+j35UmjXLz3pQPa59CBSBFzIpzRVe06cX/xxAW2PtZXlg/SgCLwCoMYbWA+Xp65M+9KH4vuc/X3rb2/ysB1PjXvsGBhgWiyAQeCGT0jy0Xiqe40WFV+1leWD9KHdwPS2NAJAwqhyA8nzhC8U/XH3qU1Jzs5/1YGpKtT3099d/HUCdEXghc6xNd0ujxJMafXBbGrNY4eUGXlR4AUDCGFoPTG7fPukTn4jvW7FCOvdcP+vB1LW0FO8j8EIACLyQOc8+Kw0OxvelrcKLwKv+8tjSSIUXACSMCi9gch/7mNTTE9/36U9HA0eRTaUqvLj+IQBNvhcAVMqt7pLSV+HltjQyw6v28tjS+NRT0RM+Z870sx4AyB0CL4TqrLPK+4F0cFDauze+b9o06bzzos87OqQdO5JfH2qLwAuBIvBC5rgD66dPjz7ShAqv+hoaknbvju/LYoXXokXRL0+tHdv34IPSC1/obUkAkC8MrUeo9uwp/mGpXEeOVP9epAOBFwJFSyMyJ+0D6yUCr3rbuzd62EyhLFZ4tbZKnZ3xfbQ1AkCCqPACECICLwSKwAuZ41Z4pa2dUSLwqjd3YH1zszR/vp+1TBWD6wGghhhaDyBEDQ1Sk9PcxfUPASDwQua4gVcaK7zcGV779/MglFoqNb+rIaNXNwbXA0ANUeEFIFRc/xCgjN4SImRuS2MWKryk4vmfSE4eBtaPosILAGqIGz4AoeL6hwAReCFzslDhNW9e1FZXiLbG2nFbGrM4sH6UW+FF4AUACWJoPYBQEXghQAReyJwsDK03pritsZwnQaM6ea7wevTR6AnhAIAEcMMHIFRc/xAgAi9kThaG1ksMrq+nPFV4uYHX0FBxoAcAqBJD6wGEisALASLwQuZkoaVRIvCqJzcQynLgNXeuNHt2fB+D6wEgIdzwAQgV1z8EiMALmZOFofUSgVe99PVJTz4Z35fllkZjGFwPADXDDR+AUHH9Q4AIvJApfX3SoUPxfWmt8GKGV33s2lW8L8sVXhKD6wGgZhhaDyBULS3x7f5+P+sA6ojAC5niVndJ6Q283Aqv3/xGuvdeqafHz3ryym1nnD1bmjXLz1qS4lZ40dIIAAmhwgFAqLj+IUAEXsgUd35XY6M0Z46ftUzE2uK1/upX0uLF0rx50vnnS1u3RsdhavL0hMZRtDQCQI24Q+v7+/nHGEAYCLwQIAIvZIobIh1zjNSQsr/F3d3S0qXSX/xF6deHhqQbbpDOPjs6rru7vuvLmzw9oXGU29L40EPcjwGoP2PMScaYdxpjrjXG3GeM6TXG9Bljdhlj/tUY82ZjTJPvdVbEveGTaOsBEAYCLwQoZVEBMLG0D6zfskVavlzaubO843fujI7fsqW268qzECq8enqk/fv9rAVAmIwxfy3pEUkbJP2xpN+SNF1Sq6SFkv5I0nck3WqMyc6vGkoFXtz0AQgBgRcCROCFTHErvNI0v6u7W1q9Wurtrex9vb3R+6j0qk4eK7w6O6Ump2aCtkYAdbZAkpHUK+lbktZJ+gNJZ0m6UNLtI8edJemHxpgZPhZZsVKBF4PrAYSAwAsBIvBCpqQ18LJWuuiiysOuUb290tq1tK1Vw63wykPg1dQkLVoU38fgegB1tl/SFZJOsNZeaK292lr7U2vtHdbab0l6qaTrRo59nqRLfS20IlR4AQgVgRcClK25CwheWlsat20rv41xPPfcI91yi7RyZRIrCoO1+WxplKK2xgceGNumwgtAPVlrr5jk9SFjzHpJ50lqkbRG0sfqsbYpcYfWS9z0IQwdHdLAgPTkk/H9c+ZI7e2VnQfZROCFABF4IVPSWuG1YUNy5yHwKt+zzxZX1eWhwksqPbgeANLEWrvfGHOXorbG0yY7PhVaWor3cdOHEOzYIf3xH0vXXz+277jjpEcekaZP97Ys1BGBFwJESyMyJY0VXj090ubNyZxr06bofCiPW91ljLRwoZ+1JM0dXE+FF4CUGr2DGva6inIZUxx6cdOHENx/f/SY8ELvfS9hV0gIvBAgAi9kShorvHbtkoaGkjnX0JC0e3cy5wqBO7C+o6P0L++zyK3wIvACkDbGmOMlnTGy+Wufa6mIe9PH0HqE4O/+Lj4sduZMaf16f+tB/RF4IUC0NCJT0hh4HTqU7PkOHkz2fHmWx4H1o9wKr927o3uyUuNnAMCTP9fYz5LXTXTgeIwxnZMc8tzAoKNHj+rIkSPVfJmYttZWmYJ/bI/29Gg4gfOiOn0FgWMf4WNt7Nqlto0bZQp2Dbz97RpsbZXq9Hef77N/TQ0Nai7YHjp8WP0Jf//5Puff0YwFpQReyIyhIenpp+P70tDSOCPhB7HPnJns+fIsrwPrpeIKL0l6+GHpjDOK9wNAvRljlkl678jmLknVTrN8bPJDIrfddpseTKDc9RxrNa1gu/vWW/Vkxn6Az6vt27f7XkIuLf7GN3T6wMBz20PNzfrRkiU6evPNXtbD99mPUx9+WEsLtvfv2aNba/h3gO9zPu1zZwylHC2NyIxnnpGGnQkhaajw6uyUGhuTOVdTU35mUNWD29KYpwqvGTOk44+P72NwPYA0MMbMl3SDol+cWklrrbWH/a6qfEPNzbHthoIgAMib5p4eLXJCjf955St1dO5cTyuCL8NN8VoXrn0IARVeyIxSYXIaKrxmzZJWry6eA1qN1auj86E8ea7wkqK2xsKnhzPHC4DLGNMkKYm7lnXW2qvL+HozJf27pNFWxA9Za388ha872ZW7Q9LtkrRs2TKd5vZ7V6F17lxpz57ntl90xhkaOuecKZ8X1enr63uuEmT58uVqo3c/UU2f+ISaClrLbEODTvjMZ9Rxyil1XQffZ/8aH388tj1v+nSdk/C1j+9z/iVRaV1PBF7IDHd+18yZxbMXfVm/PpnAi9mhlcnzDC8pamu89dax7Yz9+wIgZ4wxbZL+VdLvjuz6rLX2b6dyTmvtrkm+5nOft7a2atq0aRMcXSbnBqzFWimJ82LK2trakvkeI3LokPSVr8R2mTe9SW2/8zueFhTh++yJMzelYWCgpt8Hvs/51JqWG/AyEXghM9I4sH7UypXS4sXSzp3Vn2PJEmnFisSWlHulnmiZt8DLLWSgpRGAy1o7aIxJYrrfExO9OFJJdp2kV4zs+kdr7WUJfN3640llCMXXv148APcDH/CzFvjHtQ8BIvBCZrgtjWloZxxljLRxo7R8udTbW/n729ula66JzoPyPPFEFHoVymNLYyEqvACUYq39dS3Pb4xpkPRPks4d2XWtpHfU8mvWlNtiw00f8ujoUekzn4nv+9//W1q6tPTxyD8CLwSIofXIjDRXeElSV5e0eXMUXlWivT16X1dXbdaVV+7A+tbW9P2dmCr3SY0PPVT84AYAqIMrJb1p5PPvSbrQWpvdqxE3fQjBt79dXAr/wQ/6WQvSgWsfAkTghcxIc4XXqFWrpO3bo/bGcixZEh2/alVt15VH7vyuzk6pIWdXNLfC6+jRqLINAOrFGPNZSW8b2fyRpDXW2mw/2su96SsY6A3kwtCQ9KlPxfctXy697GV+1oN0IPBCgHJ2e4g8S3uF16iuLunuu6WtW6U1a4pfb2yUzj8/ev2uu6jsqlbeB9ZLUkdH8Rxl2hoB1Isx5iOSLh3Z/G9Jr7PWZv8OiZs+5N3mzdL998f3Ud0Frn0IEDO8kBlZCbykaBbXypXRxwtfKP3yl2OvffGL0jvf6Wtl+eG2NOYx8DImamssfBjCQw9Fv6QFgFoyxrxH0l+NbO6W9H5Jp5iJh03el4nqL276kGfWSp/8ZHzfC18ovfrVftaD9ODahwAReCEzstDSWIq7TjonkuFWeOVtYP2o006LB15UeAGokzcUfL5Q0k/KeM8pkh6pyWqSxNB65NmWLVJ3d3zfBz7Ak5FQHHgNDETDYfM2EwQowN9uZEaWKrwKzZkT337mGT/ryJsQWhql4sH1BF4AMEVUOSDP3Oqu008vPWMD4XGvfZLU31//dQB1RIUXMiOrFV5z58a3n33Wzzryxm1pzHOFV6GHHvKzDgBhsdau9L2GmmFoPbLmrLOkPXsmP66/v/g3xPv2SSefHH3e0SHt2JH8+pANLS3F+/r7i6tegRwh8EIm9PZKR47E92WlwssNvKjwmrrDh4sDUCq8AABlocILWbNnj7R7d3XvffZZftuKSKkKL65/yDlaGpEJ7i+rpOwEXm5LIz9zTN2uXcX7Qqnw2rdP6unxsxYAyAUCLwAhIvBCgAi8kAluNU9zszRrlp+1VIoKr+S587vmzJFmzvSzllpbtKh4zixtjQAwBQytBxAiAi8EiMALmeBWeB17bHYeNkPglbxQBtZL0c8mnZ3xfbQ1AsAUUOEFIEQEXggQgRcywa3wyko7o0RLYy24A+vzHHhJDK4HgEQxtB5AiBoapCZnhDeBF3KOwAuZUKrCKyuo8EqeW+GV1/ldoxhcDwAJosILQKi4/iEwBF7IBDfwylKFlxt4HTnCvy1TRYWXn3UAQC5wwwcgVFz/EBgCL2SC29KYpQovt6VRoq1xqkKr8HIDLyq8AGAKGFoPIFQEXggMgRcyIU8VXhJtjVNhbVhD66XilsZHH5UGBvysBQAyjxs+AKHi+ofAEHghE7I8tL65WWpvj+8j8Kre009HbaGF8h54uRVeQ0PFbZ0AgDIxtB5AqAi8EBgCL2RClofWSzypMUludZcx0oIFftZSL/PmFf8doq0RAKrEDR+AUHH9Q2AIvJAJWW5plHhSY5LcwGvBgqiKLu/ctkYG1wNAlbjhAxAq9/rX3+9nHUCdEHgh9QYHiwOirFd4EXhVz23ly/vA+lEMrgeAhDC0HkCoWlri21z/kHMEXki9/fuL92W9wouWxur09Ejd3fF9eZ/fNcqt8CLwAoAquRUOQ0PRb9cAIO+ocEVgCLyQem47oyQdc0z91zEVtDRWz1pp61ZpzZpoltU3vxl//Re/iF631s/66sWt8KKlEQCq5N7wSdz0AQgDgRcCQ+CF1HOf0DhnTvZmNtHSWJ3ubmnpUunss6Ubb4x+Ce+6//7o9aVLi6u/8qRUS2PeQz4AqAkCLwChIvBCYAi8kHpZH1gv0dJYjS1bpOXLpZ07yzt+587o+C1barsuX9yWxoMHS7f7AgAmQeAFIFQEXghMk+8FAJNxK7yyNrBeoqWxUt3d0urVUm9vZe/r7Y3et3271NVVm7X5cuKJUlNTfMzMgw9m878HAPDKHVovcdOHdOvoiP588klpYGBsf3OzdPzxlZ8H4SLwQmAIvJB6eajwoqWxfNZKF11Uedg1qrdXWrtWuusuyZhk1+ZTY6O0aJH0wANj+x58UFq2zNuSACCbqPBC1uzYIX3ve9K558b3f/vb0vnn+1kTsonAC4GhpRGpl4fAi5bG8m3bVn4b43juuUe65ZZElpMqDK4HgAQ0NUkNzo/AfX1+1gKUw1rp4x+P7/vt35Ze/3o/60F2EXghMAReSD1aGsOyYUO6zpMmpQbXAwCqwE0fsuTHP5Zuuy2+70Mfisq/gUpw7UNgCLyQenmo8HJbGg8cKP3EwdD19EibNydzrk2bovPliTu4ngovAKgSN33Ikk98Ir69aJH05jd7WQoyjmsfAkPghdTLY4WXlL8wJgm7diUXBA4NSbt3J3OutKDCCwAS4g6u56YPafXf/y1t3Rrfd8UV0cB6oFIEXggMgRdSLw8VXqUCL9oaix06lOz5Dh5M9ny+uRVeu3dLR474WQsAZBo3fcgKt7prwQLp4ou9LAU54F77+vv9rAOoEwIvpJq1+Qi8pk+PZuQWIvAqNmNGsuebOTPZ8/nmBl6S9MgjdV8GAGSfe9PH0Hqk0Z13Sv/xH/F9l19eXKEIlKulJb5N2I+cI/BCqh08KA0MxPdlsaXRGJ7UWI7OzuTmrzY1SQsXJnOutJgxQ5o/P76PtkYAqAIVXsgCt7rr2GOlt7/dz1qQD1z7EBgCL6SaW90lZbPCS+JJjeWYNUtavTqZc61eHZ0vbxhcDwAJ4KYPaferX0VP4Cl06aVSe7uf9SAfuPYhMAReSDV3YH1ra3b/nXef1EjgVdr69ek6T9owuB4AEsDQeqTdJz8ZzfYYNXu29K53+VsP8oHAC4Eh8EKqlZrfZYyftUwVLY3lWblSWrx4audYskRasSKR5aSOW+FF4AUAVeCmD2n20EPSd74T3/fud0ehFzAVXPsQGAIvpFoeBtaPoqWxPMZIGzdWX8nX3i5dc012g9HJuBVetDQCQBUYWo80+9SnpKGhse3p06X3vtffepAfBF4IDIEXUs1tacziwPpRtDSWr6tL2ry58tCrvT16X1dXbdaVBqUCr+FhP2sBgMzipg9ptWuXdPXV8X1/8ifZ/iEY6cG1D4Eh8EKq5bnCi5bGia1aJW3fXn5745Il0fGrVtV2Xb65LY1Hj0pPPOFnLQCQWdz0Ia0+/Wmpv39su6VFuuwyf+tBvnDtQ2AIvJBqearwoqWxcl1d0t13S1u3SkuXFr/e1CSdf370+l135buya1RHhzRtWnwfc7wAoEIMrUcaPfmk9LWvxfddcom0YIGf9SB/CLwQGAIvpFqeKrxoaayOMdEge7dy64/+SNq/X7ruuuj1vM7schlTXOW1ZYt0771ST4+fNQFA5nDThzT63OekI0fGthsbpfe/3996kD9c+xCYJt8LACaSp8CLlsap2bs3vv2850mzZvlZi0/WFj+k6eMfjz4aG6XVq6X168MKAQGgYgytR72cdZa0Z8/kxw0PFx/X2ir9wR9En3d0SDt2JL8+hMW99g0MRH/3GqiDQT4ReCHVaGnEqCefjG8ff7yfdfjU3S1ddJG0c2fp14eGpBtuiD4V2RGrAAAgAElEQVQWL46edhlCmycAVIwqB9TLnj3S7t3Vvffw4egDSIp77ZOi0KvUfiAHiHKRanmq8CrV0mitn7VkUeiB15Yt0vLl44ddrp07o+O3bKntugAgkwi8AISoVLDF9Q85RuCF1OrvL55JlKcKr8FBfmlXCTfwmj/fzzp86O6OWhV7eyt7X29v9L7u7tqsCwAyi6H1AELU0lK8j+sfcozAC6nltjNK2a7wcgMvibbGcg0Ph1vhZW3Uxlhp2DWqt1dau5ZqQgCIYYYXgBBR4YXAEHghtdx2RmOkefP8rCUJpQasE3iV55lnovlUhUIJvLZtK7+NcTz33CPdcksiywGAfKClEUCICLwQGAIvpJZb4TVvXvQUuqxqbCx+uh5PaiyPW90lhRN4bdiQrvMAQC4QeAEIEYEXAkPghdTK08D6UTypsTp798a3Z88O42EyPT3S5s3JnGvTpuKZeAAQLAIvACFqaJCamuL7uP4hxwi8kFpu4JXlgfWjSj2pEZMLdX7Xrl3FrZzVGhqq/qnoAJA7DK0HECoCfwSEwAup5bY05rHCi5bG8oT6hMZDh5I938GDyZ4PADKLofUAQkXghYAQeCG1aGnEKLelMZQKrxkzkj3fzJnJng8AMosbPgCh4vqHgBB4IbXcCi9aGsMVaktjZ2dyD2poapIWLkzmXACQedzwAQgV1z8EhMALqRVChRctjeUJtaVx1ixp9epkzrV6dXQ+AICY4QUgXAReCAiBF1Irj0PraWmsTqgtjZK0fn26zgMAucANH4BQude//n4/6wDqgMALqZXHofW0NFYn1JZGSVq5Ulq8eGrnWLJEWrEikeUAQD6UCrys9bMWAKgnAn8EhMALqTQ8nM/Ai5bG6oQceBkjbdwotbdX9/72dumaa6LzAABGuDd8ElUOAMLQ0hLfJvBCjhF4IZUee0waGorvo6UxTEeOSAcPxveFMsNrVFeXtHlz5aFXe3v0vq6u2qwLADKrVODFTR+AEFDhhYAQeCE1rJW2bpXWrJFOO6349fe+N3o9yx0HtDRWzq3uksKq8Bq1apW0fXv57Y1LlkTHr1pV23UBQCa5Q+slbvoAhIHACwFp8r0AQJK6u6WLLpJ27hz/mM2bo4/Fi6MWryxWrbgVXocPRx0UbmUxxriBV3NzcXAYiq4u6e67pVtukb78ZWnTpqj9d5QxUWC8fn00s4s2RgAYBxVeqJeOjujPJ5+UBgbG9jc2jr1WyXmAqSLwQkAIvODdli3S6tVSb295x+/cKS1fHoVfWateKRXUPPtsmBVL5So1vyvkIMeYaJD9ypXSV78qvfOdY6+dcYZ03XW+VgYAGVIq8Orrq/86kH87dkg33yy9+tXx/f/4j9LFF3tZEgJH4IWA0NIIr7q7Kwu7RvX2Ru/r7q7NumrFrfCSGFw/mb1749uEg2Oe97z49uOP+1kHAGROqdJqbvpQC9ZKf/3X8X2nnCJdcIGf9QAEXggIgRe8sTZqY6w07BrV2yutXZutmV6trdK0afF9zPGaWMhPaJzMiSfGt599Vjp0yM9aACBTGhqiHvlC3PShFrZtk3760/i+D32o+O8fUC8EXggIgRe82bZt4pld5bjnnmieUZYwuL4ybuAV2hMaJ9LZWbxv1676rwMAMskdXM9NH2rBre466aToN76ALwReCAiBF7zZsCFd56kXt62RlsaJ0dI4vunTpXnz4vsIvACgTNz0oda2b49+w1vogx/kaUXwi2sfAkLgBS96eqKh80nYtCk6X1a4gRcVXhOjpXFiblvjY4/5WQcAZI5708fQeiTtYx+Lby9cKK1b52ctwCgCLwSEwAte7NolDQ0lc66hIWn37mTOVQ+0NFaGlsaJuW2NVHgBQJm46UMt3Xqr9MMfxvddcUXpJ4QC9eT+Hezv97MOoA4IvOBF0oO1Dx5M9ny1REtjZajwmhgVXgBQJQIv1JJb3dXRIb3tbX7WAhTi2oeAEHjBixkzkj3fzJnJnq+WaGks3/Cw9NRT8X0EXnFuhReBFwCUiaH1qJXbb5e+//34vve/v/hR3YAP7gw5rn3IMQIveNHZKTU2JnOupqZoJEJW0NJYvqefLm59paUxzq3woqURAMpElQNqxa3uOv546R3v8LMWwMW1DwEh8IIXs2ZJq1cnc67Vq6PzZQUtjeVz2xkl6bjj6r+ONKPCCwCqxNB61MKdd0o33RTfd/nl0aOVgTQg8EJACLzgzfr16TpPvdDSWL69e+Pbc+bwJG+XW+F14EC2ZtoBgDfc9KEWPv7x+PYxx0jvfKeftQClcO1DQAi84M3KldLixVM7x5Il0ooViSynbmhpLB8D6yfnVnhJtDUCQFm46UPS7r5b2rQpvu9970t+eC0wFVz7EBACL3hjjLRxo9TeXt3729ula66JzpMltDSWzw28mN9VbNq06JfHhQi8AKAMDK1H0tzqrjlzpHe/289agPEQeCEgBF7wqqtL2ry58tCrvT16X1dXbdZVS6UCr+FhP2tJO7elkQqv0ty2RuZ4AUAZuOlDku69V7r++vi+Sy/N1qBZhIFrHwLS5HsBwKpV0vbt0kUXSTt3Tn78kiVRZVcWwy6puKXRWqmnp3g/aGksV2en9ItfjG0TeAFAGRhaj3KcdZa0Z8/kxz39dPRD3ShjpCuvlL72tWi7o0PasaM2awQqQeCFgBB4IRW6uqKxB7fcEg2h/9Wv4q83NUVPY1y/PprZlbU2xkJuhZcUVXkReBWjpbE8boUXLY0AUAZu+lCOPXuk3bsrf5+10uOPJ78eYKq49iEgtDQiNYyJBtm/8pXx/W94g7R/v3TdddHrWQ67pGhuaWNjfB+D60ujpbE87uB6KrwAoAzc9AEIEdc+BITAC6mzf398+/TT8zX+wBie1FguWhrLQ4UXAFSBofUAQuQGXgMD8XZcIEcIvJA6Tz8d3543z886aoknNZaHlsbyUOEFAFWgygFAiNxrnyT199d/HUAdEHghdUIMvKjwKnb4sHToUHwfFV6luRVePT3RBwBgAgytBxCiUoEXgT9yisALqRNC4EVL4+Tc6i6JwGs8boWXRFsjAEyKCi8AIWppKd7H9Q85ReCF1HFneB1zjJ911BItjZNzA6+WFmn2bD9rSbu2NunYY+P7CLwAYBIEXgBCRIUXAkLghVQZGioOf/JY4UVL4+RKDazP+hM6a8lta2SOFwBMgqH1AEJE4IWAEHghVUpVOuUx8KKlcXJ798a3aWecGIPrAaBCVHgBCBGBFwJC4IVUced3SfkMvGhpnFypCi+Mz63woqURACbB0HoAIWpokJqa4vsIvJBTBF5IFTfwamuTpk3zs5ZaoqVxcm7gNX++n3VkBRVeAFAhKrwAhIrrHwJB4IVUCWFgvURLYzloaawMFV4AUCFu+ACEiusfAtE0+SFA/bgVXnlsZ5RoaSwHLY2VYWg9gKQZY14r6cUjH6dKOk7SbEmHJD0kaZukr1lr7/O1xilhaD2AUBF4IRAEXkiVUAOvZ56RrOUphIVoaayM29J48KDU0yPNmuVnPQCyzRjTJOl747w8R1LXyMd7jDEfttb+bd0WlxRu+ACEiusfAkHghVQJJfByWxr7+6UjR6Tp0/2sJ41oaazMwoXF+x57TFq8uP5rAZAbBxRVcd2mqKLrCUmHJS2QtFLSWxVVfH3SGPOstfarfpZZJYbWAwgVgRcCQeCFVAllhpdb4SVFbY0EXpGhIWnfvvg+Aq+JtbVJxx0nPfXU2D4CLwDVstYOGmOOsdYOjXPIvxljvijpDklzJf21MebrExyfPu4N39BQ9NHY6Gc9AFAv7vWvv9/POoAaY2g9UiWUCq/Zs4v3Mbh+zNNPS8PD8X20NE6OwfUAkjRZeGWtfVjStSObx0l6fs0XlST3hk+iygFAGKjwQiCo8EKqhBJ4NTVJM2dGc5ZGEXiNced3SdKxx9Z/HVnT2Sl1d49tM7geQB30FnzeNu5RaeQOrZeimz7KrVGoo0M6cEA6dKh4fyXVgB0dya4LmIqWlvg2gRdyisALqRJK4CVFbY2FgRdPahzjzu+aO7f432UUo8ILQD0ZY6ZJet3I5rCk+z0up3KlKryY4wXXTTdJp54a3/f2t0tXXulnPUASqPBCIAi8kCqhzPCSohDnf/5nbJsKrzE8obE67pMaqfACkDRjTLOkEyS9TNIVkk4feemb1tqD475x/PN1TnLIc2UxR48e1ZEjRyr9EuMbHtY0Z1ffgQOy7pNlUFN9BSFjXwoDx+ZPflJNBeuyTU06eumlskn+XQxA2r/PoWlpalJhfeLAoUMaTODvNN/n/DuasXCUwAupElKFl/vzNIHXGDfwYmB9eajwAlALxphFkh6e4JAfSrqsytOXHc3fdtttevDBB6v8MsXM4KD+yNn30x//WIdKPfYWdbF9+3bfS4hpfeYZrXIquR59xSv0y/vuk+67z9Oqsi9t3+cQveTAAZ1QsP3Azp26/+abE/0afJ/zaZ/7ZLGUY2g9UmNoqLitL8+Bl/ukRloax7gtjQRe5XEDr8cek6z1sxYAQdgv6c2SXmOtPeB7MZWyjY2yxsT2NQwMeFoN0uj0zZvVWPD0uuHGRt2/Zo3HFQHJGGpujm03DA56WglQW1R4ITUOHCi+Oc9z4EWF1/io8KqO29J46FD03xXdOQCmaLekpSOfN0laKOk1ki6RtEHSqcaYT1pbVcR+4iSvd0i6XZKWLVum0047rYovMYG2NqmgjeelXV2yZ52V7NfAhPr6+p6rBFm+fLnaSj1MwIe9e9W2ZUts1/CFF+rlF17oaUHZltrvc6Car79e+slPnts+deFCnXTOOVM+L9/n/Euy0roeCLyQGm47o5TvwMut8CLwGsMMr+qU6sLZtYvAC8gzY0yTpCTKktZZa68u9YK1dkDSPQW7fiHp340xX5e0VdInFM3yemulX9RaO2HztSmowGptbdW0ae7UrSlqbY0FXm2SlPTXQNna2tqS/x5X60tfiv3dUFOTmj78YTWlZX0Zlqrvc6icp9E2Dw2pOeHvCd/nfGot9cCXFKOlEanhDqxva8v3k8FpaRwfLY3VaW0t/v+KwfUAasVae5ekvxjZXGeMmXp5QL3xpDKUsnevtGFDfN/atdIpp/hZD5A0rn0IBBVeSI2QBtZLtDROhJbG6p14Yvz/PwbXA/lmrR00xpyRwKmeqPJ9/6qorVGS1khKdupxrXHTh1I+/el4dVdjo/ShD/lbD5A0rn0IRN0CL2PMSZL+VNJrJZ0k6aikByRdJ2mDtfZwAl/jBZLeLWmlovkSQ4qe/vPvkr5krf2fMs5hFP3AdoGk35V0nKRhSXsk/VzRY7d/MNW1olhogRctjeOjpbF6nZ3SHXeMbVPhBeSftfbXHr/8UwWfn+xtFdXipg+uJ5+Uvvzl+L61a6VTT/WzHqAWuPYhEHUJvIwxr5X0bUmzC3ZPl/TikY+3GWP+l7X2oSl8jY9K+ktJxnnpd0Y+3mGMudhau3mCc8yW9C+KAjPXKSMfbzTGXCvpImttf4njUKXQAy9aGiO9vdFHISq8yuc+qZEKLwA1Vjg98JC3VVTLHajMTR/+/u+Lq7v+3//ztx6gFtzAq5/bWuRTzWd4GWPOVFTFNVvRD0L/T9LLJL1S0tdHDvttRcNPZ1T5NT4g6cOKwq4nJL1P0u+NfLxPUXXWLEnfNca8bIJTfVdjYdfDktZLermksyX9uaR9I6+9UdI/VLNWjM+d4XXMMX7WUS+0NJbmVndJBF6VcJ/USIUXgBo7v+Dzu72tolruTV9fn591IB2efLJ4dtdFF1HdhfyhwguBqEeF1+cUVXMNSjrHWntrwWs/Nsb8RtLfSXq+onDqrys5uTFmoaSPjGw+LunF1trHCw65zRhznaJ2xAWSNhhjuqy1w855flfSH45sPiTphdbagwWHbB05zy8lzZH0J8aYj1hrC0v5MQWhV3gdOiQNDEjNzX7WkxZu4NXaKs2a5WctWUSFF4AkGGPOk3SbtXbc2V7GmOWKfuEoRT/nfbcea0sUN30o9OlPS4cLpqxQ3YW84tqHQNS0wssY82KNVUxd5YRdoz4j6Vcjn7/XGFPp7f6bJI3+F/tXTtglSbLW7pb0VyObZ2os2Cr0+wWff84Ju0bP8z+Svjmy2SBpWYVrxQRCD7wk6cCB+q8jbUoNrDduozLG5QZejz0mWetnLQAy7TxJjxhjNhlj3mWMWWmMeaExZpkx5i3GmO9K2ipptDr/Y9ba+/wtt0rc9GHUU08Vz+668ELptNP8rAeopZaW+DbXPuRUrSu8ziv4/JulDrDWDhtjNkr6pKS5igKyLRV8jRcXfP79CY77z4LP1ygaZF+o8L/6iWaJPVjweeu4R6FioQVebkujFLU1Hnts/deSJnv3xrdpZ6yM29LY2xvNhysVsALAJFokrR75GM8RSX9prf1MfZaUMAIvjKK6CyHh2odA1DrwevnIn72S7pjguFsKPv8DVRZ4FcYie8c9Kv7aihKv31/w+USN+oW/5rl/3KNQsdACr2nTon9rCv99YY4XT2icqoULi/ft2kXgBaBil0v6D0VzTLskdUg6XtGTq5+WtFPSjyVtnKjtMfUYWg+pdHXX//k/0umn+1kPUGsEXghErQOvM0b+fMBaOzjBcYWP0z5j3KNKK3ye22xJ+8c5rvAJkYuMMdOttQW/xtEPJD0iaZGkPzPGfMNaG3tWnDGmU9LFI5u3WmuzN5w1xUIbWi9FIcSePWPbPKmxdEsjytfSEoWEhZVyjz0mLV3qb00Assdau0/RQ4eu872WmmJofb6ddVb8B63xHDhQ/Ijo//zPsbLpjg5px47k1wf4QuCFQNQs8DLGtEkabc6acGyytfYZY0yvpHZJJ050bAm/0ljr5ApJm8Y5bnnh8iR1qqBCy1p71BhzgaSbFFVx/dIY8/eS7pHULOl3Jb1fUdvlI5LWVbjO0cBsIh2VnjNPQqvwkqK2xsKfw6jwoqUxCSeeGP//kcH1ADAObvrybc8eaffu6t7r/kAC5AnXPgSilhVeMws+P1TG8aOB14zJDnT8q6QPjnz+YWPMf1hrY7+eGwnfPuy8b6azLWvtfxtjXiTpT0c+vuocckjR8PsNI7/5rNRjVbwnCMPDxWFPCIGX22ZG4EVLYxI6O+O/iH6MKw8AlMZNH4AQce1DIGr5lMbCoQj9ZRw/+l/ZtEq+iLX2Nkn/NrJ5pqRbjDGvNMZMH/l4paIZYWc66yj6OsYYo2ig/RpFVV2uGYqeCvnaStaIyR04UPwkuRADL1oaaWlMgvukRiq8AGAc3PQBCBHXPgSilhVehVVWLeMeNWb0v7ojVXyttYqe0Ph7kl4i6YcljrldUXviaCviwcIXjTENkv5Z0vkju66S9GVFLZONkl6oqKXxjyRdbYx5gbX2sgrXOVm7ZsfIOoPjzu+Swpjh5T6pkQovWhqT4D6pkQovABgHQ+sBhIjAC4GoZYVXYaBUTpti+8if5bQ/xlhrn1U0v+tSSfc6L++R9DFFT4ycVbDfjRbWayzs+oi19m3W2juttX3W2l5r7U+tta+T9E8jx7zPGFNRpZe1dtdEHyNrDZI7v6u1NXqKYd5R4RU3NCTtc5qFCbwq51Z4EXgBwDgYWg8gRAReCETNAq+ROVqjt64TDms3xszVWOBV1a2ZtbbfWvs5a+1iSXMk/ZakBZIWWGs/bK09KukFI4cfVPEg/UsKXvvbCb7Uhwo+f1s1a0WxUgPrjfGzlnpihlfc/v3Fra3M8KpcqZZG9/9XAIC46QMQJvfaNzDAD4vIpVpWeElRO6AknW6Mmah98vkl3lM1a+0Ba+1vrLVPWBv9l2uMmS/p9JFDbrfWDjtvO2Pkz3tHwrHxzr1L0mjT1fPHOw6VCfEJjRItjS53fpckHXts8T5MzG1pPHyYv1sAUBKBF4AQudc+SeovZ+w2kC21Drx+MvJnu6TfneC4FQWf/7RGa3mzpNGaoetKvD448mc5c81GB9oPTngUyhZq4EVLY5w7v2vePKm51OMjMKEFC4orJBlcDwAlEHgBCFGpwIvrH3Ko1oHXvxR8vq7UASPD4i8a2XxW0takF2GMmSXpioKv8Z0Shz088ucSY8ycEq+PnmuJpNE45uHxjkNl3KH1IQysl2hpdLkVXrQzVqelpfj/O+Z4AUAJDK0HEKKWEs+U4/qHHKpp4GWt/bmk/xrZvMQY89ISh12msXbCz1trBwpfNMZcbIyxIx8fKfV1jDEnGGNK1oEYY2ZKulHRExAl6XJr7cESh9408merpM8aUzxByhjTJukLBbu+V+pronKhVnjR0hjnBl4MrK9eqTleAAAHQ+sBhIgKLwSi1hVekvRnko4oahW82RjzQWPM7xljXmGMuVLS340cd7+kz1T5NS6Q9Jgx5lPGmP9ljHmRMWaFMeYKSfdIetXIcd+01l41zjk+K2n0dnudpO3GmAuMMb9rjHmJMeYdku6Q9IqRY34l6eoq1wtHqIFXqZbGkOdFui2NBF7Vc+d4UeEFACXQ0gggRAReCEQ586qmxFp7pzHmjZK+JWmWpL8pcdj9kl47TuVVueZLev/Ih2tQUZj2oRKvja5znzHm1ZI2STpF0h+MfJTyC0nnWWuZ7JcQAq/I8LB08KA0a5af9fhGS2NyqPACgDIQeAEIEYEXAlHzwEuSrLU3GWNeoKja67WSOiX1S3pA0vWSvmStPTyFL7FJUpuksyWdJul4SUcl7ZJ0s6SrrLU7y1jnL4wxSyWtlfQ6SS9QNK/LKqr+unNkvde6rZeYmlBneLktjVLU1kjgFaHCq3pu4EWFFwCUQOAFIEQNDVJTkzRY8Aw2rn/IoboEXpJkrX1U0vtGPip539WapHXQWvuQpI+PfEyJtbZX0oaRD9RJqBVeM2dG/94MD4/te/ZZ6eST/a3JJ1oak0NLIwCUgaH1AELV2krghdyrxwwvYFKhBl4NDQyuL0RLY3JKtTSGPB8OAEpiaD2AUFHhigAQeMG74eHikCeUwEsi8CpES2Ny3AqvI0eKg2UACB43fABCxfUPASDwgnc9PfGWPimcGV5S6Sc1hujQIemwM8mPwKt6CxZIxsT3MbgeABylbvgohwUQAgIvBKBuM7yA8bgD66WwKrzcwCvUCi+3uksi8JqK5mapo0N64omxfY89Jp15pr81AUDqlHpS2cCA1NJS/7UgeR0d0Z9PPSX1FzxcvaVFOu64ys8D5Il7/Sv8bwTICQIveOe2WbW0SNOn+1mLD7Q0RtzAq60tGuqP6p14YjzwosILABzu0HopqnIg8MqHHTukO+6Qzjorvn/jRumNb/SzJiAtqPBCAGhphHelBta7rVh5RktjpNT8rpD+HtQCT2oEgEmUqvBicH2+fPGL8e0FC6TXv97PWoA0IfBCAAi84F2oT2gcRUtjZO/e+DbtjFPnPqmRwAsAHKUCL2768uPJJ6Xvfje+b/36qO8fCJ1bycq1DzlE4AXv3BleIQ2sl2hpHOVWeM2f72cdeeIGXrQ0AoCDwCvfvva1+Fyi1lbp7W/3tx4gTajwQgAIvOAdFV7xbVoaI1R4TR0tjQAwCQKv/OrvlzZsiO9785srG1YP5BmBFwJA4AXvCLzi26FWeNHSmLxSFV7W+lkLAKRSQ0Nxexs3fflw443xJ7dI0p/+qZ+1AGlE4IUAEHjBu9ADL1oaI7Q0Js+t8OrrK24hBoDguTd9DK3Phy98Ib798pdLL3qRn7UAaUTghQAQeMG70Gd40dIYoaUxeSecEBUvFGKOFwA4uOnLn5//XPrZz+L7qO4C4rj2IQAEXvCOCq/4dl9fmL9cpqUxec3NUkdHfB9zvADAwU1f/nzxi/Htzk7pvPP8rAVIK659CACBF7wLPfByK7yk8NoaBweLK/1oaUwGT2oEgElw05cve/ZI114b3/eud0lNTX7WA6QV1z4EgMAL3oUeeLkVXlJ4bY379xcPU6fCKxk8qREAJtHWFt/mpi/brrxSGhgY225rk972Nn/rAdKKwAsBIPCCV8PDBF7NzVJ7e3xfaBVebjujJB17bP3XkUduhReBFwA4GFqfH/390le+Et93wQX8UAGU4l77+vv9rAOoIQIveNXTE4VehUIbWi8VtzWGFni5A+uPOYbOg6TQ0ggAk6DKIT+uv774t2jveY+ftQBpx7UPASDwgldudZcUXoWXxJMa3cCL+V3JoaURACbBTV8+WCt9/vPxfStWSGee6Wc9QNpx7UMACLzglRt4lWrvC4E7xyv0Ci/mdyWnVIWXOy8NAILGTV8+3HabdPvt8X1/9md+1gJkAdc+BIDAC16Vmt9ljJ+1+BR6S6PbfUDglRy3wuvoUWnfPj9rAYBUcofWM8Mrm77whfj2ySdL557rZy1AFrS0xLcJvJBDBF7wav/++HaI87skWhppaaydE06QGpwrPXO8AKAAVQ7Z9/jj0fyuQu96FwNBgYlw7UMACLzgVehPaBxFS2N8mwqv5DQ1RaFXIeZ4AUABbvqy76tflQYHx7anTZMuucTfeoAs4NqHABB4wSsCrwgtjfFtAq9kuXO8CLwAoAA3fdl29GgUeBW68MJwf6gEysW1DwEg8IJXBF4RWhrj27Q0JqvU4HoAwAhu+rLt2mulp56K73vPe/ysBcgSrn0IAI3t8IrAKxJyS6O1tDTWmju4ngovACjA0Pp0Oussac+eyY9zf4hobZVe85qx7Y4OaceOZNcG5AGBFwJA4AWvGFofCbml8dAh6ciR+D4Cr2RR4QUAE+CmL5327JF27678fUePVvc+IDRc+xAAWhrhFRVekZBbGt1fzEq0NCaNCi8AmAA3fQBCxLUPASDwglcEXhG3pbGnRxoa8rOWenMDr2nTpPZ2P2vJq1IVXtb6WQsApA43fQBC5F77+vv9rAOoIQIveEXgFXErvKRwqrxKPaHRGD9rySu3wqu/v3i+LwAEi8ALQFuSyGsAACAASURBVIhKBV78RhQ5Q+AFb6wtDryY4TUmlMCLJzTW3gknSI2N8X3M8QKAEQytBxAiN/CSqPJC7hB4wZtSbXuhVnhNmyY1N8f3hTK4nic01l5jYxR6FWKOFwCMoMILQIhKBV5c/5AzBF7wxq3uksINvIwJ90mNBF714c7xIvACgBEEXgBC1NJSvI/rH3KGwAveuIFXU5M0Y4aftaRBqE9qLDXDC8krNbgeACACLwBhosILASDwgjel5neFPKzcfVJjqBVezPCqDXdwPRVeADCCwAtAiAi8EAACL3izf398O9R2xlG0NEao8KoNKrwAYBwMrQcQIgIvBIDAC964FV4EXvFtWhqRJCq8AGAcVHgBCFFDQzRTphDXP+QMgRe8IfCKC7GlcXCwuNKPlsbacCu8du+Whof9rAUAUoXAC0CouP4h5wi84A2BV1yILY379hXvo8KrNtwKr/5+6amn/KwFAFKFGz4AoeL6h5wj8II3bmXPMcf4WUdahNjS6LYzGsPfg1rp6JAaG+P7aGsEABXP8BoclIaG/KwFAOqJwAs5R+CVUvffL/X0+F5FbVHhFRdiS6M7sP7YY4tHCSAZjY3SggXxfQyuBwAxuBlAuNzrX3+/n3UANULglVKvfGUUAJ1/vrR1q2St7xUlj8ArLsSWRp7QWF/uHC8qvABABF4AwkWFF3KOwCvFhoakG26Qzj5bWrpU6u72vaJkEXjF0dJI4FVrbuBFhRcAiMALQLgIvJBzNA9lxM6d0vLl0ubN0qpVvleTDDfwCn12U6mWRmujuVZ55VZ48YTG2nIH11PhBQAi8Eqrjo7oz+Fh6Ykn4q/NmSO1t1d2HgDFCLyQcwReGdLbK61eLW3fLnV1+V7N1FhLhZfLrfAaGpIOHZJmzvSznnqgpbG+qPACgBLcofUSN31psGNH9OfnPiddeunY/rY26eGHi39TCKByBF7IOVoaM6a3V1q7NvszvQ4ejB6CVIjAq3hf3tsaCbzqiwovACihqam4nLqvz89aEGet9I1vxPe9/vWEXUBSWlri2wReyBkCrwy65x7pllt8r2Jq3OouicBr1qzin7fzPrieGV715VZ47d4ddYoAQNCMocohrXbskO6+O77vkkv8rAXII659yDkCr4zasMH3CqbGDbyamvLduleOhgZp9uz4vtAqvJjhVVtuhdfAQPH3AACCxE1fOrnVXYsWSStX+lgJkE9c+5BzBF4ZtWmT1NPjexXV278/vj1vXr6Hs5fLbWvMc4WXtbQ01tv8+VG4XIi2RgAQN31pdPiw9J3vxPetWxf9hhBAMrj2Ief4FyOjhoaidqSsYmB9aaWe1JhXBw8Wj0gh8KqtxkZpwYL4PgbXA4CKB9dz0+ef+9tdY6SLL/a2HCCXCLyQcwReGXbwoO8VVI/AqzS3wivPLY2lWuloaaw9d44XFV4AoOKbPobW++e2M65aJZ10kp+1AHlF4IWcI/DKsCzPvCLwKi2kCi838Jo+XWpv97OWkLiBFxVeACBu+tLmoYekrVvj+976Vj9rAfKMax9yjsAro5qapIULfa+ieu4Mr2OO8bOOtAlphhdPaPTDHVxPhRcAiJu+tPnmN+Pb8+ZJ553nZy1AnrnXvv5+P+sAaoTAK6NWr5ZmzfK9iupR4VVayC2NtDPWBxVeAFACgVd6DA1JV18d33fBBcXfIwBTx7UPOdc0+SFIo/Xrfa9gagi8Sgu5pZEKr/qgwgv10NMThamHDkkzZkR/77L8SxoEgKH16bFlS/FvYy65xM9agLwj8ELOUeGVQUuWSCtW+F7F1BB4lRZSSyOBlx9uhdfu3dEv04GpsjYaubNmTXRNX7xYWrYs+nPePOn886PXrfW9UqAEhtanhzusvqtLOvNMP2sB8o7ACzlH4JUx7e3SNddET2bOMgKv0kJqaXRneNHSWB9uhdfgYOknZgKV6O6Wli6Vzj5buvHG4hB1aEi64Ybo9aVLo+OBVOGmLx327ZP+5V/i+xhWD9QO1z7kHIFXhrS3S5s3R7/oyjqG1pdGSyNqbf58qbk5vo+2RkzFli3S8uXSzp3lHb9zZ3T8li21XRdQEW76UqHx2mulgYGxHa2t0lve4m9BQN61tMS3ufYhZwi8MmLJEmn7dmnVKt8rmTprqfAaDy2NqLWGhuInvDK4HtXq7o4eotLbW9n7enuj91HphdQg8PLPWjVdc0183+tfX/zDEYDkcO1DzhF4pdxpp0UzT+66Kx+VXVI0xHhwML6PwCvi/kx35Eh+/92hpdEfBtcjCdZKF11Uedg1qrdXWruWmV5ICYbWezf7wQfVcM898Z20MwK1ReCFnCPwSrnTT5dWrsz+zK5CbnWXROA1ym1plPI5x2tgoPjvARVe9eMOrqfCC9XYtq38Nsbx3HOPdMstiSwHmBqG1nt38g9/6Ow4ORr8B6B2CLyQcwReKffQQ75XkDx3fldjI4+rH1Wqaj+PbY379hXvI/CqHyq8kIQNG9J1HmBKuOnzquHoUXVu3x7fuW5d1IcPoHa49iHn+Fck5R55pPhpV1lXan5XnirYpqKlRZo+Pb4vjxVebjtjQwMPLqgnt8KLwAuV6umJHqKShE2bovMBXnHT59WCn/1MzYcPj+0wRrr4Ym/rAYLBtQ85R+CVcgMD+Ws3YmD9xEJ4UqM7sP7YY6NKP9SHW+GVt2sMam/XruR+GTM0JO3ency5gKpx0+fVSW4746teFbU0Aqgtrn3IOQKvDHjwQd8rSBaB18RCeFIjT2j0y63w2r07f5WkqK1Dh5I938GDyZ4PqBhD670xjzyi4+6+O76TYfVAfbiBV3+/n3UANULglQF5m+PlBl60ssW5gVcILY0EXvXlVngNDRV/T4CJzJiR7Plmzize19Mj3Xuv9POfR3/S9oiaYmi9N40bN8Z3zJ0rnXeen8UAoaHCCzlH4JUBeavwcofWU+EVF2JL4/z5ftYRquOPl5qb4/uY44VKdHYm14bc1CQtXBh9bq20dau0Zk30b8PixdKyZdGf8+ZJ558fvW5tMl8beA43fX4MDanxW9+K77vgguKKOwC1UarCi39kkSMEXhmQ9wovAq84WhpRaw0NxVVet95KBQ3KN2uWtHp1Mud60YuiCq/ubmnpUunss6Ubbyxusx0akm64IXp96dLoePhhjPlDY4wt+PiI7zVNGYGXHz/6kRrcQZK0MwL14177JNoakStNvheAyeWtwovAa2IhtDQSePljrbRtW3G4deml0uWXRyHG+vXSypU8PRUTW78+CqCm6vbbo9DrN7+RCh/SNpGdO6Xly6MnRa5aNfU1oHzGmHZJX/G9jsQReCXrrLOkPXsmP875odA2N8uce+7Yjo4OaceOhBcH4DmlAq+jR0vvBzKIwCsDqPAKSwgtje68KFoa66O7W7rooigsKGW0guaGG6IWso0bpa6u+q4R2XHSSVFr7MDA1M/1y19W/p7e3iig3b6dv6d19jFJJ0t6UlJ+fl3httAxw2tq9uyp6vGrZmCAx7YC9TRe4AXkBC2NGfDMM/kKPdwZXgytj6OlEbWwZUtUETNe2OUaraDZsqW260I2PfBAVAWYRNg1Fb290tq1jBupF2NMl6Q/lXRU0l94Xk6yqPACEKKWluJ9XP+QIwReKeW2EuWpyosKr4nlvaXRWgKveuvujiphensre99oBQ2zksIy2dMR779fWrFCcsfuVKK9XfroR5P5b/+ee6Rbbpn6eTAxY0yjpK9LapT0N5J+43dFCSPwAhAiKryQcwReKTX6xKpReZnjZS2B12Ty3tLY01P87ygtjbVjbdTGWGnYNYoKmjCU+3TEe++Nwq7HH4+//7d+S3r+88v7WkuWRG2IH/6w9LKXJbP+DRuSOQ8mdKmkLkn3S/qU57Ukj8ALQIgIvJBzBF4pdfLJ8e28VHj19ha3wBB4xeW9pdGt7pKk446r/zpCsW1b+W2M46GCJt8qeTriC15QPIe6qyt6yue9946FZo2N8WOamsZCs7vuit7T0yPddFMy/xs2beIpo7VkjFkk6aMjm+uttfm7GyLwAhCihoboH+lCXP+QIwytT6mTTpJ++tOx7bxUeLnVXRIzvFxu4NXTIw0PR/8e5YEbeLW3Rx+ojaQqXzZsiGY2IV+2bKms3dUNw178YukHPxi7bq1cGX309ERzpw8elGbOjKqWZ82Kv3fXruLzVWtoKPp67tdAYr4iabqkb1trf+R7MTVRami9tTyuFkD+tbZKg4Nj2wReyBECr5RatCi+nZcKL3dgfWMjNygut6XRWunAgeIgLKvcJzQyv6t2enqkzZuTOddoBQ3/veZHtbPdRi1dGgVms2cXvzZr1uR/Vw4dqu7rjufgwWTPh4gx5i2SXiPpWUmX1eD8nZMc0jH6ydGjR3XkyJGklxCtw1o5kZeO9PSUHuiMSbVZqySiQmut+mr0PUd99RU8+bSPp6CmSltrq0zBDwNHe3o0XOV/d3yf8+9oxgJRAq+UOumk+HZeK7zmzuWXp65SwdYzz+Qn8HIrvJjfVTtU0GA8U53tJkV/J6by92HGjOrfW8rMmcmeD5IxZp6kfxjZ/KC1du9Ex1fpsXIPvO222/RgjX4gmvbkkzrH2ffj739fg9Om1eTr5d05R48qif/n+o4e1c0335zAmZAm27dv970EFHi1E/jfceuteiqBoJnvcz7t27fP9xIqkpMmqfxxZ3g99pjU3+9nLUliYP3k2tuLW+nz9KRGntBYP1TQYDxJzHa7996pzXbr7Cye9VWtpqbih70gEZ+WdLyk2yR9zfNaamq4ubloX4M7dBQAcmjIuf5x7UOeUOGVUm6F1/Cw9Oij0vOe52c9SSHwmpwxUVtjYXiep8H1tDTWDxU0GE8aZrvNmhW1VN5ww9TXsXp1uNWHxpgmSUncnayz1l5dcN6VktZJGpL0J9ba4QS+RiknTvJ6h6TbJWnZsmU67bTTarOKEv/QrnzpS0lSq9RW6slvVZ7nnHPc2jtkUV9f33MVP8uXL1ebOzcP3rTOmRP7jfSLFi/WcJX/3fF9zr9aVVrXCoFXSs2eHYVBhQHRgw9mP/ByZ3gxsL60uXPzG3jR0lg/oxU0SbQ1UkGTH2ma7bZ+fTKB1/r1Uz8HxhhjWiVdObL5BWvtL2r1tay1uyZZy3Oft7a2alodWwynGSPR0lidhOZVGGPq+j1HfbS1tfF9TRMnlGq1NpFrH9/nfGpN6Bca9ULglWKnnhoPvPIwuJ4Kr/K487poaUQ1qKBBKWma7bZypbR48dTaK5cskVasqP79WWetHTTGnJHAqZ4o+Pz1kn5L0qCke40xbypx/O8UfL6k4JjbrLUPJ7Ce+ir1A3zGBvMCQFXc6x/XPuQIgVeKnXaatGPH2HbGqgdLIvAqj/ukxjxXeBF41RYVNHClababMdLGjdLy5dUN0G9vl665hoefWGt/nfApR+9+miR9vYzj3zDyIUVtkNkLvBoaolLWwcGxfdz0AQgBgRdyjKH1KXbqqfFtKrzC4VZ45Snwcmd40dJYW6MVNFMRegVN3qRttltXV9Ri2d5e+XvPOy96P5AIbvoAhKilJb7NtQ85QuCVYu5c1jxWeDHDq7S8tjT29xeHd1R41dZoBU01YYJEBU0epfHpiKtWSdu3Vx7OXnutdNddU//6iLPWXm2tNRN9SHpFwVs+WvDa1Z6WPXUEXgBCxLUPOUbglWKlKrys9bOWpLhD66nwKi2vLY2Fg/hHEXjVXrUVNO3t0fuooMmX0dluSUhytltXl3T33dLWrdKaNcWhXFNTFIw1FPzkMjgoXXJJvAsNqJr7NLG+Pj/rAIB6IvBCjhF4pZhb4dXbWzz/KGtoaSxPXlsa3XbGhgaq/Oql0gqaJUui41etqu264EdSM9mSnu1mTNSGe/310b8X994r3XZb9Of+/dLNN0sf+ED8PTt2SJ//fLLrQKC46QMQIq59yDGG1qfYwoVSc7M0MDC276GHsjvzyFoCr3LltaXRDWyPOy5erYHaGq2gueUW6ctfjqq33Kf1nXyydPXV0cwu2hjzKwtPR5w1q3T12F/+pXTjjdJ998X3ve510umn1249CAA3fcnp6Bj7/MCB+NMyGhqkE054btNaq76R/6/bWltlCv/xKTwPgNrg2occ41YzxRobpVNOie/L8hyvw4ejGU6FCLxKy2tLI09o9M+toPnzP4+/ftJJ0euEXfmW5dlubW3SVVfFv/aRI9L//b/Zb/uHZ9z0JWfHDmnXrug3tW6r6BVXRK+NfPQ98IBuvuoq3XzVVep74IHYa7HHlQOoDa59yDECr5TL05Ma3fldEu1s4wmlpZHAy69Zs6SXvjS+7+GH/awF9Tc62829F51MGma7/f7vS+96V3zftm3SP/6jl+UEyVq7rWBQ/Ud8rycR3PQl73vfKx7guW6dn7UAKI1rH3KMwCvl8vSkRredsaEhuWHHeeNWeD37bD4qF9wKr6y25+aJW0W6ezc/54Rk1Spp6dLyj0/TbLe/+ZuoIrHQ5ZdHf4eBqjC0Pnnf+EZ8++Uvl573PD9rAVCaG3i5LTlAhhF4pVyeKrzcwGvuXOY3jcet8BoYiFpCs27Xrvj27Nl+1oExbuBlrfToo37Wgvq7807p9tvj+9zrclOTdP750dMT77orPU/tnDlTuvLK+L6eHumd78zHLwjgAVUOyXr8cen734/ve+tb/awFwPi49iHHiBtSLs8VXszvGp8beEnZbWu0NrpRXrNG+ud/jr925ZVjN9LcoPoxe3bxf4u0NYbjM5+Jb8+fL+3ZU/x0xOuuS+dst9e8Rrroovi+m26K1itFAdi990o//3n0Z09P/deIDOGmL1kbN0rDw2PbM2ZEPwwASBeufcgxAq+Ucyu8nngiu5U+7gwv5neNr1TlUxaf1NjdHbVLnX129FQ1N9QaHpZuuCF6fenS6HjUn1vlReAVhl27pGuvje9797ujp6eecYb0kpdEf6a99fyzny2eB/iOd0jnnhuFuYsXS8uWRX/Om0fIjglw05cca4vbGd/4xij0ApAuXPuQYwReKecGXlJ2b0ap8CpfY2PxTWbWKry2bJGWL5d27izv+J07o+O3bKntulAsT63TKN8XviANDo5tT5sWtQNmzTHHSF/6UnzfgQPRrOyhofj+oSFCdkyAm77k/PSn0m9+E99HOyOQTlz7kGMEXinX3l482DurN6MEXpXJ8pMau7ul1aul3t7K3tfbG72Pm9D6osIrPD09xfOv1q3LbuXtmjXSy15W2XsI2VGEofXJcau7fvu3ix8LDCAdWlri2wReyBECrwzIyxwvAq/KlHpSYxZYG83UqTTsGtXbK61dS7tRPRF4heeqq+LzrIyRLr3U33qm6s47pV/8ovL3EbIjhiqHZBw6NDZIb9S6dekbAgggwrUPOUbglQF5aTdyA6+sVhLUS1YrvLZtK7+NcTz33CPdcksiy0EZ8nKNQXkGB6XPfz6+73Wvk04/3c96pmo0ZK92viUhO57DTV8yrr8+/luvxsbip0sASA+ufcgxAq8MyEuFlzu0ngqviWU18NqwIV3nweTcCq9nnslORSEqd+ON0qOPxvddfrmftSSBkB2J4aYvGW474x/+oXTCCX7WAmByXPuQYwReGZCX6gtaGiuTxZbGnh5p8+ZkzrVpU7zlCrVz0knFnSa0NeaTtdKnPx3ft2xZ5fOv0oSQHYnhpm/q7r9f+slP4vsYVg+kG9c+5BiBVwa4FV4PPywND/tZy1QQeFUmixVeu3YVPxWtWkND0u7dyZwLE2ttlTo74/sIvPLpv/5L2rEjvu+yy7I7WoeQHYliaP3UffOb8e3jjpNe+1o/awFQHgIv5BiBVwa4FV5Hj0qPP+5nLdWylsCrUm7glYUKr0OHkj3fwYPJng/jY3B9GD7zmfj2okXR0PasImRHorjpm5rBQemaa+L7Lryw+AlwANKFax9yjMArAzo6pGnT4vuyNsfr8OHiaydD6yfmtjRmocJrxoxkzzdzZrLnw/jcwCurrdMY3333STfdFN936aVSU5Of9SSBkB2J4qZvan7wA+mJJ+L71q3zsxYA5XOvff39ftYB1ACBVwYYk/05Xm51l0SF12Sy2NLY2Rk9jCkJTU3SwoXJnAuTc68xVHjlzz/8Q/xJhHPmZH+0DiE7EkXgNTXusPqXvERassTPWgCUj2sfcozAKyOy/qRGN/AyRpo9289asiKLLY2zZiXXHrV6dXQ+1Actjfn21FPFnUbveEfygVG9EbIjUdz0Ve+pp6R/+7f4Pqq7gGwoVeFV+BsyIMMIvDIibxVec+dKDfztm1AWWxolaf36dJ0H5SkVeGXx4Rgo7Stfic/fbmqS3vMef+tJCiE7EsXQ+up961vRDK9RbW3Sm97kbz0AyucGXhJtjcgNIoeMyFuFF/O7JudWePX2SgMDftZSiZUrpcWLp3aOJUukFSsSWQ7KVOrhGHv2+FkLktXXJ33pS/F9b3lLfqqZCNmRGCq8qmNt8dMZ3/CG4t/cAUinUoEX1z/kBIFXRmS9wmv//vg287sm5wZeUjbaGo2RNm6U2ture397e9R6ZUyy68LEOjqKf96hrTEf/umfom6jQu97n5+11AIhOxJD4FWdO+6Q7r47vi/rAwKBkBB4IccIvDLCrfDat0/q6fGzlmq4FV4EXpMr9YvRrLQ1dnVJmzdXHnq1t0fv6+qqzbowvoYGadGi+L6sBesoNjwsffaz8X2vepV05pl+1lMLhOxIDIFXddxh9YsWRUk0gGxoaSnex/UvbGedFQ1KLfHR9vKX+15dRQi8MmLRouIfxrN0M0rgVbm2tuJxIlkJvCRp1Spp+/byKy+WLImOX7WqtuvC+HhSYz709Ej33iv9/OfSlVdKv/51/PXLLvOzrloiZEciCLwqd+SI9J3vxPetW8egViBLqPCCa88eaffukh9m717fq6sI/xplRGtrFKoWytIcLwKv6mTxSY2FurqiLodXvKL0601N0vnnS1u3SnfdxU2nbzypMbusjf47WrMmur4uXiwtW1Y8l2rxYunVr/azxlqrNGSXosowQnY8h6H1ldu8WTpwYGzbGGntWn/rAVA5Ai/kGIFXhmR5jpc7w4uh9eXJ6pMaCxkT/QK40GWXRRUo+/dL110XdT7QTuSfG3hl6RoTsu5uaelS6eyzpRtvlIaGxj/22Wf/P3t3HidXWeb9/3t3Op2lkyZhzUYg7JCEYBMWEZOIBBfcGokG1AAqI5NnZpgZUWd8fqPO42/0JzKj4wzMjGyScQQlJODCFjALjj5ApoUsIIRFQifpkIXQSWfppPv+/XF30XVOVXfXck6d7fN+vfrVde46dc5NKpzUueq6rlv6/e9rN7daywXZc8G/IUMG3v/hh2szLySE/6bv0CGWqx2Mv5zx4oul446LZi4AKlNX576FzkfACylBwCtBkrxSIxlelfFneCUx4CVJf/yjd3vOHOn006Wmpihmg/5Q0pg8y5ZJs2ZJ69eXtv+mTW7/ZcvCnVeUjHHXmHvvdf/2PPec9OST7re/Wf9dd7EaKfKQ5VCeP/5Revxx7xjN6oFkoqQbKUXAK0GSnOFFwKsySS9plFx2l/+G0t8cHfHgz/Bqa5O6uqKZCwbX2iq1tEidneW9rrPTva61NZx5xUlTkwuun3uu+/2lL3l783Z1ST/4QXTzQ8wQ8HIGaFbs+TnzTO/rjHEp3LnnZ86MZv4AykfACylVP/guiAsyvLInDSWNGzcWjlHtEE/+gJe10muvSSefHM180D9rpQULyg925XR2ujY7a9Zkq5x43Dj353bbbX1j//Zv0t/+rTR6dHTzQkwQ8HJyzYrLZa20eXPw8wEQPv/1j288kRJkeCWIP8Prtddce4kk8Ae86OFVmjSUNL72mnf78MO5sYyrMWMK/85R1hhPK1aUXsbYn3XrpJUrA5lOotxwgzfIt2uXNwCGDPM3rZdoXA8gG8jwQkoR8EoQf4ZXd3fx7Jm42bu38PMiGV6lSUNJo79/F+WM8cZKjclwyy3xOk6SnHqq9NGPese+9z3p4MFo5oMYIcMLQFYR8EJKEfBKkMMPL2zynYQ+Xv7sLomAV6nSUNJIwCtZWKkx/jo6pKVLgznWkiXueFnzpS95t19/XfrpT6OZC2Jk6NDCMW76AGQBAS+kFAGvBDEmmX28/AEvY6TDDotmLknjz/DavDl5N6cEvJKFlRrjr63NZfgGobu7slY9SXfBBdK73uUdu/FG14IIGWYMN30AsolrH1KKgFfCJHGlRn/Aa8wYaciQaOaSFNZKy5dLt97qHX/+eZcdN2+eez4JN2f+gBcN6+ONksb427Mn2OPt3h3s8ZLiy1/2bq9dKz3ySDRzQYz4b/ro4QUgCwh4IaUIeCVMEjO8duzwbtOwfmCtrdL06dJFF0m/+13h893d0uLF7vnp093+ceZvWk+GV7xR0hh/o0YFe7ysLiLxoQ9Jp53mHfvud6OZC2LE37iemz4AWdDQ4N3m2oeUIOCVMGnI8KJ/V/+WLZNmzSp99bX1693+y5aFO69KHThQuEI5Aa94819jdu5MXhlt2k2aFFyWbH29NHFiMMdKmro6t2Jjvl//Wlq9Opr5ICbIcgCQRVz7kFIEvBKmWIZX3MvaCHiVprVVammROjvLe11np3tdHDO9iq0iSkljvB13nGtjk4+yxnhpanL/zwehpaVwMZQs+fSnpXHjvGNkeWUcN30AsohrH1KKgFfC+LMvOjqKr4IYJwS8BmettGBB+cGunM5O6aqr4hf89PfvGjuWBQvibtgwacIE71gSMkmzZuHCeB0nqYYNk66/3ju2eDF/5zONmz4AWcS1DylFwCthJk8uLGWJex8vf8CLHl6FVqwovYyxP+vWSStXBjKdwNCwPplYqTH+5syRpk6t7hjTpkmzZwcynUS77jpvX7SeHumf/im6+SBiNK0HkEUEPOj2hAAAIABJREFUvJAv6BWSIkTAK2Hq6wuDBnH/JtrftJ4Mr0K33BKv4wSFhvXJxEqN8WeMtGiR1NhY2esbG6W77iosX82iMWOkL3zBO3bHHdL27dHMBxGjaT2ALCLghZzHHpPeeivqWQSGgFcCJW2lRkoaB9bRIS1dGsyxliyJV4Nxf4YXAa9kYKXGZGhudteOkSPLe11jo3tdc3M480qi6693Xyjl7Nsn3XxzdPNBhLjpA5BF/mtfV1c080C0XntNmj+/cPyII9wqRxMnyh5zTO3nVQUCXgmUtJUaCXgNrK1N6u4O5ljd3dKmTcEcKwgEvJKJksbkmDtXuvba0vefNk1atcq9Dn2OPVa68krv2L/8i7R3bzTzQYQIeAHIIq592LdPuuyywvKsb37Tpb23tUltbdr/xBPRzK9CBLwSKOkZXvTw8gq6RHr37mCPVw0CXslUrKQxbgsioI//c4e/TLG+Xpo3T1q+XFqzhsyu/txwg3d7xw7pzjvjdU1FDXDTByCLuPZlm7XSn/6p1NrqHf/oR6WvfjWaOQWkfvBdEDdJy/Cih9fA8pslB2H06GCPV6muLmnzZu8YTeuTwR/w2r9fam+Xxo+PZj7o34svFn42WbpUOuUUF6gZPdploDc1RTO/JJk+XfrAB6SHHuob+8pX3BeeyBCa1gPIIgJe6TFzpvvgXo7OTmnXLu/YsGGu4WtdsnOkCHglkD/Dq63NXZP816k42Lev8LMiAS+vSZPcyptBlDXW17ub2zh4/fXCrCAyvJJhwgSpocHbvuHVVwl4xdHdd3u3jzlG+tCHClfzRWm+/GVvwKuzM7q5ICI0rQeQRQS80qO9PZgeN2PGSIcdVv1xIkbAK4H8GV7WutKxU0+NZDoD8pczSgS8/JqapJYWafHi6o/V0hKfTA5/OeNhh7nrJuKvrs4FJ198sW/s1VelCy6IbEoowtrCgNcnPkGwqxpdXe7vf09P1DNBZLjpk8aN63tc7Fv/8eNL+8Y//zgA4o1rH/zq0xEqSsd/RcY0NUlHHuldMv3ll5MR8DKGoEcxCxcGE/BauLD6YwSF/l3JNmWKN+AV99LpLHrmGemFF7xjV1wRzVzSoLXV9Wol2JVx3PRJq1f3PT7nHO/2ZZdJ991X+zkBCBfXPqRUsgsyMywpfbz8/bvGjCH7oJg5c6SpU6s7xrRp0uzZgUwnEP6AF/27koWVGuPPn911/PHS+edHMpXEs1ZasIASRoibvnxr1niDXZL0uc9FMxcA4Wpo8G5n+dqHVCHglVBJWanRn+FFOWNxxkiLFkmNjZW9vrHR9RT0r84WJTK8kq3YSo2Ij54e6Z57vGPz58frGpAkK1ZI69dHPQvEAk3r+9xxh3d7wgTpkkuimQuAcBHsR0oR8EqopGR4EfAqXXOzW12t3KBXY6N7XXNzOPOq1GuvebcJeCWLP+AV12tMVv32t25hiHyUM1bulluingFig6b1zoED0o9/7B27+urU9HQB4EPACylFwCuhyPBKp7lzpVWrSi9vnDbN7T93brjzqgQZXsnmD6q3tUkHD0YzFxTylzOecYY0fXo0c0m6jg73pQEgiZu+nJ//vLAvxTXXRDMXAOHj2oeUIuCVUMUyvKyNZi4D8Qe8jjgimnkkSXOztHattHy5dPnlxXueHXmke37NmvhldklupTP/argEvJLFn+HV0yNt3BjNXOB16JB0773esSuuoJyxUm1tUnd31LNAbHDT5/jLGWfPlk46KZq5AAgf1z6kFAGvhPJneO3bJ7W3RzOXgfi/HCTDqzTGuEb2997rgob/8i/e5+vr3fNxvcFtaytc6Yym9ckydqx02GHeMcoa4+Hxx6Vt27xj8+dHM5c02LMn6hkgVrjpc/XSjzziHaNZPZBu/mtfV1c08wACRsAroSZMKLwuxfFmlJLG6jU1Se95j3esvV3avTua+ZTCX844erQLoCBZWKkxnvzljOecQ+JFNUaNinoGiBWa1rtVcPLLBpqapI9/PLr5AAgfwX6kFAGvhKqrKyw5imMfLwJewTjxxMJsrji+3znFGtbHNRsN/WOlxvjZv7+w3xTN6qszaVLx0nFkVNab1vf0FJYzXnGFNHJkNPMBUBsEvJBSNQt4GWMmG2NuMsY8b4zpNMbsNMY8ZYy5wRgTyL+ixpgzjTE/NMa82HuODmPMemPMjcaYyYO89kfGGFvmz9VBzLtSSVipkYBXMIYPl4491ju2YUM0cykFDevTIQnXmKx58EHXZD3HGOmTn4xuPmnQ1CS1tEQ9C8RG1m/6Vqwo/Hbjs5+NZCoAaqhYSWMcG0QDZapJwMsYc6mkNZK+KOk0SSMljZV0jqTvSmo1xpzQ/xFKOsffS3pG0rWSTu49x2hJZ0j6kqS1xpigP9K+EPDxypKElRr9PbxoWl85f8kSAS+EjQyv+PnJT7zbs2e7EndUZ+HCqGeA2Mh6wMuf3TVtmqubBpBu/mufRB8vpELoAS9jzAxJP5N0mKQ9kv63pAskvVfSrb27nSrpV8aYijppGGP+RtLXJBlJWyT9taTze3/+WlK7pCZJdxtjLujnMP9b0vRBfmZJyrXi3mCt/V0l8w1KErIvyPAKzskne7dfeimaeZTCH/CiYX0yEfCKl44O6Ze/9I5RzhiMOXOkqVOjngViIcsBr127pPvu84599rP0JACyoFjAK0vXP6RWfQ3O8X25bKtDki7xBYl+bYzZIOlGucyvv5b0f8o5uDFmoqRv9G5ulnSOtXZz3i5PGmN+JukpSRMk3WKMabbWetaQs9ZukrRpkHP9qfqChIvKmWcY4p7htW+f+8lHwKtyZHih1vxB9e3b3WIJo0dHM5+su/9+72fP+nr6SAfFGGnRImnWLKmzM+rZIFJZblp/993e/96hQ6VPfzq6+QCoHQJeSKlQM7yMMedImtO7eXs/GVH/KOn53sd/aYwZWuZp5kvK/R/6dV+wS9Lbwayv927OkPSBMs+RsyB3SEn/WeExAuO/Gd26NV4f1N98s3CMgFflkpLhdeiQtMkXOibglUzFMvPI8oqOf3XG972PMvEgNTe7BQEaG6OeCSKV5ab1t9/u3f7IR6SjjopmLgBqq6GhcCxL1z+kVtgljR/Le3xnsR16M61y2VJj1RcgK1V+Y4GHBtjv4bzHl5d5DhljTpYrkZSkldba1wbavxb85UZSvMoa/eWMkjR2bO3nkRb+gFd7u8u2iZu2Nqm72ztGwCuZhg8v7A9FwCsa27ZJy5Z5xyhnDN7cudKqVZQ3ZlqxksYsNG5+9lnpf/7HO/a5z0UzFwC1R4ZXevC+eYRd0vju3t+dkv5ngP1W5j2+UNKy/nYsIj9naOsA++U/N7uM4+csyHsceTmj5FaIHj9e2rKlb+yVV6Tp06ObUz5/w/oxY1j6vRonnODKbvI/d7/0kvSOd0Q3p2L85YyjRpHZl2QnnCBtzsubjVNQPUsWL/YGkkeMkD760ejmk2bNzdLatdLKldLNN0tLlkg9PYO/Dinhv+mz1qUuDy23ACFh/M3qJ06ULrkkmrkAqD0CXumwdavrx+h39NHl/zs2blwwc4pY2AGv03t/v2StPTTAfn8o8ppS5RfxHSZpRz/7HZb3+HhjzEhr7d5STmCMMZJyTQz2Slpc5hxDc+KJ3oBXnPp40bA+WMOHS8ceK23c2DeWhIDXccfR7zbJpkyRfvObvm0yvKLhL2f88IddMBnhMMY1sp8zR3r+eemMM6KeEWqmv5u+NAe8DhyQfvxj79jVV/MtJZAldXWuOeihvFt2Al7J0tMjXXWV9z2UpO99T/rLv4xmTjEQWkmjMWa4pCN7N9sG2tda+6b6AlfHlnmq5/MeD5S5NSt/epImlXGOWZKO73281FpbUSGZMWbSQD+Syg6jxnmlRgJewfOXNcaxcf1rvmJfyhmTjZUao/f669ITT3jHKGesHRZpyBh/Dy8p/Y3rH3ig8EPbNddEMxcA0cnyKrVp8E//JD3yiHfs0kul66+PZj4xEWYPr/yPiHtK2D8X8Cr3O+sH8h5/rTfQ5tE79jXfcDkfYfPLGe8q43V+rw/y83S5B4zzSo0EvIKXhJUaWaExXeIcVM+Kn/7Uu33YYdIHKl16BcDAsljW4y9nnDOn8AMmgPTzX/+6uqKZB8r39NPS3/6td2z8eOnOOzNfahNmwCs/8FTK/y25TxMjyjmJtfZJST/v3ZwhaaUx5r3GmJG9P++V6xE2wzePks7TGyzLNbnfJOnxcuYXtjjfjPp7eLGaWPWSsFIjAa908Wd4/fGP2ejfHCf+csbLLit+Tw4gAFkLeG3cKD36qHfss5+NZi4AokWGVzJ1dLjU//xSRmNcqTor7Ybawys//7vIOqcFcv+H7avgXFfJrdB4vqRzJT1WZJ+nJa2TlMvRLrUs8WOSmnof/7h3VclKDVauOU5lZnn5v4B79VXX2DgObRfI8ApeEkoaCXiliz/gtXev9MYb0jHHRDOfrHnxRam11TtGOSMQoqwFvO66y/stRlOT9PGPRzcfANEh4BUPM2dK7e2l7//mm+4Der5Ro6Qvf1lavTrYuSVQmAGv/IBSKWWKjb2/Syl/9LDW7jLGzJa0UNK1kvLby7ZLulXSP0j6r7zxN0s8fGCrM1prB+xlZipIN/RneB08KG3aJE2eXPahAkfAK3j+ksatW11Qv6mp+P61duiQ6zeU77jjopkLgjFhgtTQ4M1qf+UVAl614s/uOvpo6T3viWYuQCYMGeJ+8pdFTetNX09PYTnjlVe6ZcABZA8Br3hob3c39NXYvbu8oFmKhVbSaK3dL2l77+aADeKNMWPVF/B6faB9Bzhfl7X2+9baqZLGSDpF0gRJE6y1X7PWHpB0Zu/uuzVII/3eeR0jKbcm8/9Ya5+rZG5hOvpoqbHROxaXPl4EvIJ3wgmFZdhxeb8lafNm7z2CRIZX0g0ZUhi0pHF9bVhbGPD6xCfcIkoAQuRvXJ/WpvUrVhSmZVPOCGQXAS+kUJg9vKS+FRRPMsYM9BH9tCKvqZi19i1r7QZr7RZrXZ52b/Aqlx/zdImliZ+SlCsOrKZZfWiMiW8fL3/Aix5e1Rs+XDrWVxgbp7JG/+fmkSOlI48suisShJUao/HMM9ILL3jHKGcEaiArN3233+7dnj7dldIAyKasXPuQKWEHvH7T+7tR0tkD7Dc77/F/hzSXKyTlcmN+VuJrcuWMByXdPdCOUYrrSo3+pvVkeAUjzo3ri/XvyvjCIKkQ16B62vmzu447TnrnO6OZC5ApWbjpe/NN6b77vGOf/Sz/aANZloVrHzIn7MKI+yXl1se8RtKT/h2MMXXqCyztkrQ86EkYY5okfSXvHD8p4TXT5VZ2lKQHrbXbB9o/SnG9GaWkMRwnnyw9nrdWaJwzvOjflQ5keNVOR4fU1uZ+/+d/ep+bP597UaAmkn7TV0rD487Owv+uG2+Ubrqpb3vcOBoeA1nS4FtnLmnXPqCIUANe1tqnjDFPSHq3pM8ZY+6y1v7Ot9sXJZ3e+/ifrbUH8580xlwt6c7ezb+31n7Dfx5jzHhJ2/2v7X1utKT75FZAlKQbrLWlrNB4Vd7jqprVhy2OGV779xcuFkHAKxj+xvVxDnjRvysdCHiFy1rXSufmm6X77y/sg5czf35NpwVkV9IDXpU2PN6yJfi5AEiOpF/7gCJq0fr2erkyxRGSHjXGfEsui2uEpPmS/qR3vxcl/WOF5/iUpBuMMXdJWilpi6QmSefLrdyYW7PwTmvt7cUP0ccYM0TSlb2bOyX9ssJ51UQcM7zeLLIGJj28ghHnksbXXvNuE/BKB/81ZuNGtyLs0KHRzCdNWlulBQuk9esH3/fTn5YWLZKam8OfF5BpWWlaDwD5CHghhUIPeFlrf2+M+aSkH8sFob5VZLcXJV1aYuZVf46R9OXeH79DcsG0r5Z4rLmSxvc+vsda21XFvELnz/DauVPatUsaMyaa+UiF/bukaOeTJv6A19atrvypqSma+eQjwyud/BlePT3S668XBsJQnmXLpJYWV1lUivXrpVmzpKVLpblzw50bkGnc9AHIIq59SKGwm9ZLkqy1v5B0pqTvyQW39sr10lot11vrHdbaavJUlkj6O7nMsY2S9kt6S9L63nOeZa39mxJXZpSkz+Q9jnU5o+T6JNX53smos7z8/bsOO0yqr0U+YQZMmVLYxycOWV7d3S7zJx8Br3QYO7YwoEpZY3VaW8sLduV0drrXtbaGMy8A4qYPQDZx7UMK1STgJUnW2testX9trT3VWttorR1rrT3HWnujtXbvAK/7kbXW9P58o599XrHW/r/W2oustcdZa0dYa8dYa6f1nrOEYhHP8T6Vd86CRvtx09AgHXusdyzqPl40rA/P8OHS5MnesTgEvDZvlg4d8o7RtD4djIln6XRSWevKGMsNduV0dkpXXeWOAyAE3PQByCL/ta8r1kVOQElqFvBCuOJ2M0rAK1z+ssY4NK73lzMOHy4dfXQkU0EIaFwfnBUrSuvZNZB166SVKwOZDgA/Al4AsohrH1KIgFdKxG2lRn/Ai4b1wYrjSo3FGtb7Sy+RXP6AV9RB9SS75ZZ4HQeAD03rAWQRAS+kEAGvlPBneP3+99Jzz7lm5lHwN60nwytYcVypkYb16ea/xpDhVZmODtd0PghLlkR3jQdSjZs+AFnEtQ8pRMArBayV9u3zjq1eLU2d6gJN8+ZJy5fXtt8LJY3himOGFwGvdKOkMRhtbW6BhyB0d0ubNgVzLAB5uOkDkEVc+5BCBLwSrrVVmj5d+uY3iz/f3S0tXixddJHbr1YrexHwCpc/w+uNN6LP9PAHvGhYny7+gNe2bdKePdHMJcmC/jPbvTvY4wEQN30AsolrH1KIgFeCLVsmzZpVevPj9evd/suWhTsviR5eYTvhhML+WFGXNZLhlW7F3k+yvMo3alSwxxs9OtjjAVDhTR89vABkAQGveIg6iyFlCHglVGur1NJS/rL2nZ3udWFnetHDK1zDhkmTJ3vHoixr7OmRNm70jhHwSpcRI6Tx471jBLzKN2mSNGRIMMeqr5cmTgzmWADy+JvWc9MHIAsIeEXvoYdI3w9YfdQTQPmslRYsKD/YldPZKV11lbRmTXir6G3f7t32f3ZE9U4+2bsyYpQZXlu2SAcPescIeKXPlCnuvc5hpcbyNTW5Lx0WL67+WC0t7nhAWIwxKyTNLmVfa2161uXlpg9AFjU0eLe59tXW669Ln/lM4fhRRxW+N6UYN676OaUAGV4JtGJF6WWM/Vm3Tlq5MpDpvM1a1xz/8ssLGylfeWU0zfPTzN/HK8oML3854/Dh0jHHRDIVhIiVGoOxcGG8jgPAh4AXgCzi2hedgwel+fMLy6S+9z3XrLmtrfyf1auj+W+JGQJeCXTLLfE6jtTXPP+ii6T77it8Pqrm+WkWp5Ua/QGvyZPDyx5EdFipMRhz5rhVdKsxbZo0u6S8GyAQqyVNH+QnPbjpA5BFXPui89WvSr/9rXespUW6/vpo5pMilDQmTEeHtHRpMMdassQdr9qSmGXLyusnlmuev3SpNHdudefOMn+GV5QljTSszwZ/wIuSxsoYIy1a5K6DlZSmNzZKd91FUBk11WmtXRf1JGqGpvUAsoiAVzR+8Qvpppu8Y1OmSHfcwYe9ABDwSpi2NpctFYTubld6WE3Aq9rm+atWSc3NlZ8/y/wBrzfeCCaAWYn8XmISAa+0KlbSaC3/FleiudkF/T/wgfKu6Y2N7nVcN4EQ0bQeQBb5A15dXdHMI8lmzpTa20vfv7tb2rq1cHz4cGnMmODmlWEEvBJmz55gj1fNIhBJaJ6fZlOmSHV1boXEnJdeiuZGmAyvbPBneO3dK23bJh19dDTzSbqxY8sLdk2b5jK7CHYBIUt6lkOuUXFnp7Rrl/e58ePdh4dyjgMgG5J+7YuD9vbCZtaV6Oio/hiQRMArcUaNCvZ4o0dX/togm+fPmVPdcbJo2DDXKys/2LRhAwEvhGfiRGnoUO+KnK+8QsCrUt/5jnd72DDp0CFvEKy+3mXDLlzoenbx5QBQA0m/6Vu92n0redZZ3oDXvHnSz34W3bwAxFvSr31AETStT5hJk6QhQ4I5Vn29u4GtVByb52dNHBrX9/QUljQed1zt54HwDRlS+N7SuL4yGzYULvDxjW9IO3dKzz0nPfmk+71jh7s/nTOHYBcidZox5mljzG5jzH5jTJsx5gFjzAJjzNCoJxe4NNz0rV7tUujzXXttNHMBkAxpuPYBPmR4JUxTk/u2f/Hi6o/V0lJ5v6c4Ns/PopNPlh57rG87isb17e2FJf5keKXXlCnev2cEvCpz000uASNn9GjpuuvcdZBrIWLomN6fnIm9Px+R9BVjzOXW2ucrObAxZtIgu7xdV3fgwAHt27evktOUpU5S/m2f3bdP+2tw3iAN/fd/93zI7znuOB244AIphv8d+/MWBdjPAgGpxfscf8ZaeToYdnVp3969ZX3jlvX3ebi1CuL7SWttbP/dOZCwQCgBrwRauDCYgNfChZW/Nm7N87PK37g+igwvf3ZXQwNtP9KMlRqr197uenHlu+46epMilnokPS7pQUnPStohabSkZklfkHS6pDMkLTfGnGut3VjBOV4vdccnn3xSL7/8cgWnKM8Ra9fqwrztrj179Oijj4Z+3qAM2bdP7//JTzxjL7zrXXox/xuymFq1alXUU0AN8D7HU+OWLbrYN/bYgw+qZ2hlibxZfJ8vOXBAIwI4zv4DB2L778727dujnkJZCHgl0Jw50tSp1fXPmjbN9YOpVJya52dZHEoa/f27jjuu9H64SJ5iKzWiPP/8z94qgYYG6S//Mrr5AAO4zFq7q8j4E8aYWyTdKukqueyv70u6rJaTC4v/5m5IfuPCBJj43/+t+rzMCltXp40XXRThjAAkQXd9YWig7uDBigNeQBwQ8EogY6RFi6RZsypbIbGx0WUXVNMPJk7N87PMn+G1bZv01lvSYYfVbg7FAl5IL3+GFwGv8rz1VmHfws98RpowIZr5IPmMMfWSgojIXGOt/VH+QD/BrtxzB40xn5d0nqTTJLUYYyZaa8tdnurYQZ4fJ+lpSTrvvPN04oknlnn48pmjjvJsD+nu1iWXXBL6eYMy7Fvf8mz3vO99mnXllRHNZnD79+9/OxNk1qxZGj58+CCvQBLxPifAG28UDF30rndJvmviQLL+Pg/390Gr4jhx/XenFpnWQSLglVDNza6HVktLeUGvxkb3umpX8ss1zw+irLHa5vlZNmWKy6bq6ekbe+kl6eyzazcHVmjMFn/Aa+NGt7JgkS8FUcR//Id3pWljpC99Kbr5ANWw1h4yxtwu6bu9Q7Ml/WSAlxQ7RttAz5u8b+eGDRumESOCKBYZhO9bI3PwoEYMG5aM9OV166SnnvIMDfnCF2rz5xaA4cOHJ2auqBzvc0wV+cZ8RF2dVOF7lcn3OaAVhowxsf2zGxZQUK9WuEVJsLlzpVWrpAULSitvnDbNZXZVG+yS4tM8P+uGDZMmT/YGnTZsIOCF8PhLGru7pddfLwyEodCBA9L3v+8da2mRTj01mvkgHXqDTqcHcKgtFb7uubzH6fj6qtiH+QMHKr7pq6nbbvNujx8vXXppNHMBkCz9XfuABCPglXDNzdLatdLKldLNN7vsLX/W1dFHSz/9qevZFeSy9nFong9X1pgfdKr1So3+pvUEvNLt8MNdCXJ+371XXyXgVYr//E9piy+k8JWvRDMXpIu19g8Rnj7ATxYxUawEJwkBr/373YUm39VXk4ILoDQEvJBCCcjNxmCMcY3s771X2rmzMINAcs8HGezKHXPq1OqOUW3zfETbuN5aAl5ZYwwrNVaiu1v67ne9Y3PmSOeeG8l0gCCdkfd4c2SzCFJSb/qWLnUfBPN97nPRzAVA8tTVFQbIk3DtAwZAwCtlmpqkD37QO/bGG1J7e/DnyjXPb2ys7PVBNM9HYeP6WmZ4bd3qvlDOR9P69GOlxvI98ID04oveMbK7kHS9DfM/mzeUjjXokxrw8pczXnSRVIMm/wBSxH/96+qKZh5AQAh4pdCJJ0ojR3rH1qwJ51y55vnlBr2Cap6PwoBXLTO8/P27hg517UKQbqzUWB5rpe98xzs2Y4b0vvdFMx+gFMaY9xhjxgzw/FBJt8mt0ChJv7DWvl6TyYUtiQGvl1+Wfv1r79i110YzFwDJ5b/+xf3aBwyCgFcK1dVJ06d7x8IKeEl9zfP9pXX9mTbN7T93bnhzyhL/n/u2bdJbb9Xm3P6A1+TJbvVOpJs/w4uSxoGtXFmwaJq+/GWyWxF7V0l63RjzX8aYa40xs4wxZxljLjTGXC/p2d59JOkNSddHNtOgNTQUjvnTmePm9tu924cfLn3sY9HMBUByEfBCyhDwSqkzz/RuP/tsuOdrbpb+7u/6f76+Xpo3T1q+3AXfyOwKzgknFK6UXquyRvp3ZRMZXuXxZ3cdf7z0iU9EMhWgXKMkXSnph5JWSvq9pCckfV9SbmXItZLeY61Nz5XAmGTd9B08KN15p3dswYLizfcBYCBJuvYBJWDZlpSaMcO7HWaGV84zz3i3L7pI+va33YpuEye6/mIIXkOD65uVH3TYsEE6++zwz+3P8CLglQ3+gNcbb0idnZX380uzZ56RHn7YO3bDDSyahkT4jqRnJL1TrjH9UZIOl3RA0lZJqyUtlrTUWtvd30ESa9gw741enG/6HnywsFnr5z8fzVwAJBsBr+rs3Rv1DODDR+6U8md4Pf+86zlYLEs/KL//vXd71ixWIKuVk04qDHjVgj/gRcP6bCgW2Hz1VVeuDK8bb/RuH3mkdM010cwFKIe19nlJz8tlc2VPkm76br3Vu/3Od1a/jDaAbErStS9uWlulN9+MehbwIeCVUv4eXgcPSi+8UDgeFGsLM7ze8Y5wzoVCJ58sLVvWt12rkkYyvLJp5Ehp3Dgijhr/AAAgAElEQVRvQgEBr0Kvvir99Kfesb/4i8JFRQDEUFJu+trapIce8o6R3QWgUkm59sXNjh3SZZcVjo8dW9kHv3Hjqp8TJBHwSq0xY1wD8Y0b+8aefTa8gNcf/yjt2uUdI+BVO1Gs1GgtPbyybMqUwoBX1nV0uHvPPXukUaOk739f6unpe76xUfpf/yu6+QEog/+mL65N6++803uhGT2aJoEAKkfAq3zd3dKVVxbeGP35n0s/+EE0c8LbCHil2IwZ3oBXmH28/OWMRxwhTZoU3vng5V+psRYBr23bpH37vGMEvLLjhBOk3/2ubzurKzVaK61YId18s3T//e4zT3+uvdYtnAYgAfwN3+N409fTU7g64xVXuIg7AFSCgFf5vv516dFHvWPvepd0003RzAcerNKYYv4+XrUMeJ11llvkCLXhz/Davr0w4y5o/nLG+nppwoRwz4n4YKVG16ph+nS3QMd99w0c7JKkuXNrMy8AAUjCTd9jjxVmFFx7bTRzAZAO/obPcbz2xckDD0j/8A/esXHjpHvvDbd5NkpGhleK+QNezz4b3rn8AS/KGWtryhSprs5b1fDSS9LMmeGd0x/wOvZYaciQ8M6HeMl6wGvZMqmlxa1OWapPfEJaupTAF5AISQh43Xabd3vGjNos0QwgvZJw7QvLzJmFK94O5NAht1S532GHSePHBzcvVIWAV4r5A17t7e7/yaOPDv5cBLyi1dDgVkjMDzrUOuBFOWO2nHCCd3vDBmn9ehf4bGqKZk610tpafrBLcvu3tEirVknNzeHMDUBA4n7Tt22bq6POd+21pNcDqE7cr31ham+XNm2q/jh79lR/DASGksYUO/nkwhYUa9cGf5433pA2b/aOEfCqvVo3rifglV3WSlu2eMf273erNB5+uDRvnrR8udsvbayVFiwoP9iV09kpXXVVOv9sgFSJe9P6RYvcEtw5w4e7pskAUA3/ta+rK5p5AAEh4JViQ4a4G9B8YfTxeuYZ7/bIkdIppwR/Hgys1o3rWaExm3J9q/q7r+rulhYvdn2tpk93+6fJihUuk60a69ZJK1cGMh0AYYmqaf3MmW7Vn8F+/uZvvK+rq3MX3dzzYaZ4A0ivLGd4IZUoaUy5M8+UVq/u2w6jj5e/nPHMM+nlFAV/htdLL4V7PjK8sqfcvlXr10uzZqWrb9UttwR3nDlzgjkWgBBEddNXaUnN3r3uBwCqQcALKUOGV8rNmOHdDiPDi/5d8VDLkkZrCwNexx0X3vkQvWr7VqUh06ujwwXvgrBkiTsegJjipg9AFnHtQ8oQ8Eo5f+P69evdghJBIuAVD/6Sxu3bpV27wjnX9u2FXyST4ZVe9K1y2tpcyWYQuruD6YsKICTc9AHIIq59SBkCXik3fbp3u6tLeuGF4I6/Z09hJhEBr2hMmeJaeOQLq6zRn901ZIg0cWI450L06FvlBL3ozu7dwR4PQIDi3rQeAMJAwAspQ8Ar5Y44ojAQEWRZ47PPerM2ijXKR200NBRmWYVV1uhvWH/ssVI9HQFTK8i+VUk2alSwxxs9OtjjAQhQVE3rASBKBLyQMgS8MiDMPl7+csYzzij8jIjaqdVKjfTvyg76VvWZNCm4BTnq68mKBGKNmz4AWcS1DylDwCsD/H28wgx4Uc4YrVqt1MgKjdlB36o+TU2uAX8QWlrc8QDEFDd9ALKIax9ShoBXBvgDXs8+G9yx/QGvs84K7tgoX1QZXgS80ou+VV4LF8brOABCwk0fgCxqaPBuc+1DwhHwygB/SeOmTdKOHdUft6vLNaLOR4ZXtGqV4eXv4UXAK73oW+U1Z440dWp1x5g2TZo9O5DpAAgLTesBZBHBfqQMAa8MOOWUwmD92rXVH/e556SDB71jZHhFyx/w2r5d2rUr2HNYS4ZXltC3yssYadEiqbGxstc3Nkp33eWOAyDGaFoPIIsIeCFlCHhlQH19YUZCEGWNzzzj3Z4yRRozpvrjonLHHy/V+f6vDrqscefOwjI3mtanF32rCjU3u0b+5Qa9Ghvd65qbw5kXgABx0wcgi7j2IWUIeGVEGI3raVgfPw0NhdlWQZc1+rO76upcFhDSi75VhebOlVatKj3oNW2a23/u3HDnBSAg3PQByCL/ta+rK5p5ROHQoahngBAQ8MoIfx8vAl7p5S9rDDrDyx/wmjRJGjo02HMgXuhbVdzkydK+ff0/X18vzZsnLV/urrlkdgEJQsALQBZl9dq3Y4frBYPUqY96AqgNf4bXunVSd3flvXl6egpLGgl4xcNJJ0mPPNK3HXTAi4b12ZPrWzVrltTZWf7r09q36v773bUwZ/hw6Te/cdfW0aNdv7I0lHACmUTTegBZlMWA16FD0ic/6T7AIXUIeGWEP+C1f78LhJx2WmXHe+UVafdu7xgBr3gIe6VGGtZnU65vVUtLeUGvNPetWrzYu33ppdLZZ0czFwABo2k9gCzKYsDri1+UHn/cOzZ0qHTUUZV9WztuXDDzQiAIeGXEUUdJ48dLW7b0ja1ZU3nAy1/OePTR7viIXq1LGmlYnx25vlULFkjr1w++/6hR0sqV6Qx27dxZ+Nlo3rxo5gIgBFm86QOAYj28rE1fmn7OHXdIP/iBd2z8eOnpp5O/tDgkEfDKlDPPLAx4feITlR2rWP+utF4Hk+akk7zbO3ZIb74pjR0bzPHJ8Mq25mZp7VoXyLr5Zpe91V8G+J49wf29i5uf/9zb23T4cOmDH4xuPgACRsALQBb5r32SC3oVG4+TmTOl9vbyXtPVJW3b5h0zxn24JdiVGgS8MuTMM729nZ59tvJj0bA+vo4/3vVmyw9CvPSSdM451R/bWgJecJ8F5sxxPx0d0qZNrsR56NC+sZw77pC++c2IJhoifznj+9/v+nYBSIliPbxqkeWQXwrT0eHtH1FXV3o6PSU1ACpRLLB14ED8A17t7e4DabXGjJHOO6/64yA2CHhliL+PVzUrNfoDXmedVfmxEKyGBldm+MorfWMbNgQT8Nq1q7B3GwGvbGtq8jZm/8xnXOZXzh13SF//uluxMC3eekt69FHv2OWXRzMXACHx39xZ69I6w16WePXqvvOddpr3H90/+zPpn/853PMDyLb+Al5ZMXJk1DNAwOqingBqZ8YM7/bGjS6AUa4tW6StW71jZHjFS1iN6/3ZXXV10qRJwRwb6XDttd7tzZulhx6KZi5h+cUvpIMH+7YbGqQPfSi6+QAIgb9pvVTbm77WVunFF71jV15Zu/MDyKaGhsKxLAW8kDoEvDLk1FMLv5hcu7b84/izu0aNKuwbhWiF1bjeH/CaMKH4v4vIrhkzCrMJb701mrmE5d57vduXXCIddlg0cwEQkqizHH7yE+/2CSdI555bu/MDyKaor31AwAh4ZUhDg3T66d6xSvp4PfOMd3vGDJfpg/jwByDDCnhRzohi/Flev/pVMG0V4qCjw9sLUaKcEUilKG/6urule+7xjl1xBasDAQgfAS+kDGGKjAmijxcN6+OvViWNBLxQzPz5UmNj33ZPj3TnndHNJ0i/+pX3c9/QodJHPhLdfACEpNhN3/79tTn3qlWuHjwf5YwAaqGurrDxKgEvJBgBr4zx9/Ei4JVO/oDXjh3Sm29Wf9zXXvNuE/BCMaNHu2SEfLff7gJfSedfnfHii6WxY6OZC4AQRZnlcPfd3u0ZM6QzzqjNuQHAf/3r6opmHkAACHhljD/Da+1alzlfqrfekl5+2TtGwCt+jj9eGjLEOxZEWSMZXiiVv6zxj3+UHn88kqkEZs8e6cEHvWOUMwIpVV9f+A9pLQJeBw4URtbJ7gJQS/6AFxleSDACXhnjD3jt3Su98krpr/f3/Bo6VJo6tfp5IVhDhxYGo4Ioa/QHvI47rvpjIp3OOafwepP05vUPPeStaBoyRProR6ObD4CQRXHT98gjhSnZ8+eHf14AyCHghRQh4JUx48ZJRx/tHSunrNFfzjh1Kqv0xVXQKzVu3Ogy/PKR4YX+GCN9/vPesfvvl7Zti2Y+QfAnXVx0kXTEEdHMBUANRHHT51+d8cILpcmTwz8vAOQQ8EKKEPDKoGoa19O/Kzn8KzVWkuFlrbR8uSvbOuGEwue/8hX3vLWVzRHp9ulPS8OH920fPCjddVd086nG3r2uYX0+yhmBlPPf9IXdtH7PHunnP/eOUc4IoNYIeCFFCHhlkD/g5S9THAgBr+SoNsOrtVWaPt1lsdx3X/Feb0uWuOenT3f7A/nGji0MCt12WzIDpA8/LHV29m3X1Ukf+1h08wFQA7W+6XvgAWnfvr7t+npp3rxwzwkAfgS8kCIEvDKo0gyvAwek557zjhHwii9/hlc5Aa9ly6RZs6T160vbf/16t/+yZaWfA9ngb17/wgvSE09EM5dq+MsZZ88uLA8HkDL5KapS+Dd9/nLGSy6Rjjwy3HMCgB8BL6QIAa8MmjHDu/3qq1JHx+CvW7dOOnTIO+YPniE+/BleO3e6n8G0tkotLd5sllJ0drrXkemFfO9+t3Tqqd6x226LZi6V2r9f+sUvvGMkXQAZUMubvm3bXMP6fJQzAogCAS+kCAGvDDr99MKVtteuHfx1/nLGk06SmpqCmxeCdfzxhe/zYH28rJUWLCg/2JXT2SlddVUyS9YQjmLN6++9t3ARsjh79FHXWifHGBfcBZBytbzpW7zY2ztgxAiWgQUQDQJeSBECXhk0bJh02mnesVLKGunflSxDhxauojhYWeOKFaWXMfZn3Tpp5crqjoF0ueoq9/cxZ/9+6b/+K7r5lMtfzvjud7sVbwGkXC2b1vvLGT/yEWnUqPDOBwD9aWjwbich4LVrV9QzQEwR8Moof1ljKQGvZ57xbhPwij9/WeNgGV633BLMeYM6DtLhqKMKG7zfemsyMgEPHChcNI3VGYGMqFWWw8aN0m9+4x2jnBFAVJKW4fWDH1RenoLUq496AojGmWd6v0wcLODV3V24miMBr/g7+WS3ulzOQBleHR3S0qXBnHfJEnc8Sl6R8/nPu1LGnDVrpKefls49N7o5leLxx6W33vKOXXZZNHMBUGO1alp/zz3e7bFjpfe/P5xzAcBgkhTw+uUvpb/6q8Lxo44qzFQrBSn8qUPAK6OKrdTY0yPV9ZPz99JLhYFzAl7xV85KjW1t3vYh1ejuljZtIuCFPhdf7Eps//jHvrFbb41/wCs/SCdJF1wgTZwYzVwA1Fitbvr85YyXX17ZjRoABMF/7evqimYeg3n2WWn+fHcTm+8nP5GuuCKaOSF2CHhllD/gtWePuxE94YTi+/v7d40fLx1zTChTQ4D8JY1/+IP03HPSpEmFwaj8ptxB2L072OMh2erqpM99Tvq7v+sbu/tu6Z/+SRo9Orp5DaSrS7r/fu8Y5YxAhtQi4LV+fWEKPTdqAKIURYbXzJlSe3vp+3d3u9Vt/d/WT5jANRQe9PDKqAkTpCOO8I4NVNZIw/rksVbascM71tEhTZ0qHX64NG+etHx5Xx+loHvjxjWIgehcc403i7SzU/rpT6Obz2CWLy/sgfrxj0czFwARqEXT+rvv9m5PmCDNmhX8eQCgVFEEvNrbXXlIqT/t7cVLU4wJf65IFAJeGWVMYZaX/wvGfAS8kqW1VZo+XfrMZ4o/393tVp676CK3X2trsIub1NdT9oVCEydKl17qHbv11mjmUgr/6oznnSdNnhzNXABEIOybPmsLA17z50tDhgR7HgAoR5J6eAGDIOCVYcX6eBVjLQGvJFm2zH05vH59afuvXy+df7504YXBzaGlhf5dKO7aa73bTz0l/fa3rtT2qafc746OaOaW79ChwkUcKGcEMibspvVPPSW98op3jNUZAUSNgBdShB5eGTZjhne7v4DXpk3S9u3eMQJe8dTa6oJN5a7Me/BgsPNYuDDY4yE9PvABV7GzeXPf2IUX9pXWSi65oaXF/T2aMyea7PSVKwtLgilnBDIm7Js+f7P6k0+WmpuDPQcAlIuAF1KEDK8M82d4vfxy8cbl/uyupia32hrixVppwYLyg11BmzZNmj072jkgvurrXdArX36wSypecltr/nLGs8+Wpkyp/TwARCjMm75DhwqbGF55Jf1nAESPgBdShIBXhp1xhreBtLXSunWF+/kDXmed5X0d4mHFitLLGAfyoQ9JjY2VvbaxUbrrLj6vo3/LlhW2rBnI+vWuRHfZsvDm5NfdLS1Z4h2jnBHIoDCb1i9fLm3d6h1jZTEAcUDACylC2CLDRoyQTjnFO1asrJH+Xclwyy3BHGfECNe7qNygV2Ojex3VGOhPruR2797yXtfZ6V5Xq0yvJ56Q3njDO0bAC8igMG/6/JH/s8+WTj01uOMDQKUIeCFFCHhlXCl9vAh4xV9HR2GD7UotWeJWo1u1Spo6tbTXTJvm9p87N5g5IH2qLbnt7JSuuqqw/DEoHR19jfN/+EPvc2edJZ10UjjnBRBjYTWt379fuu8+7xjN6gHERUODd5uAFxKMgFfG+ft4Pfusd/vNN6XXXvOOEfCKn7Y2V4YVhO5ut1BBc7O0dq2rurj88sJV0uvrpXnz3PNr1pDZhYEFUXK7bp1rJh8Ua/v+fh9+uAvwnndeYeIFzeqBjAory+HBB73L0RojffKTwRwbAKpFhhdShFUaM84f8Fqzxt0E5nowPfOM9/lhw6TTT6/N3FC6YosNVGP3bvfbGLdK3pw57rP5pk3uudGjpYkT3QIGQCmCKrm95Rb397Fara0u46yUINyiRdIHP0hQF8icsG76/Kszzp7t/lEFgDgg4IUUIeCVcf6AV0eHtHGjdNxxbttfzjhtmjR0aG3mhtKNGhXs8UaPLhxraiLAhcoEXXLb0VHd38Vly1xPsFLLKzdscI3zly6lbBfIlHKb1s+cKbW3D7yPtdLmzd6xZ56RJk3q2x43Tlq9uvR5AkCQ/Ne+rq5o5gEEgIBXxh17rDRmjLRrV9/Ys8/2H/CinDGeJk1yJYdBlDXW1/NFM4IVRsltpQGvXOP8cnuJ5Rrnr1pFpheQGeVmObS3uwtUuXbt8n4QA4AokeGFFKGHV8YZU7ysMYeAVzI0Nbmb8SC0tJDJhWCFVXJbrrg3zgcQM2E1rQeAOCPghRQh4IV+A1779kl/+IP3OQJe8bVwYbyOA+TUouS2FHFsnA8gxrjpA5BFXPuQIgS8oBkzvNu5gNfatd4ypGLZYIiPOXPcKnPVmDbN9c4FgpQruQ1CNSW3QTbOB5AB3PQByCKufUgRAl4oCGJt2CDt3VtYznjKKVJjY+3mhfIY41aTq/Q9amyU7rqrb4VOIChxKLkNo3E+gJQrt2k9AKRBsab1YfdzoDE+QkLAC5o61Rvk6OlxZT/070qe5mZ3U19u0Kux0b2OZtwIS9Qlt2E0zgeQcv6bvoMH3YckAEgz/7VPCjcg1dkp7dwZ3vGRaazSCDU2Sied5DK7ctasIeCVVHPnupXkFiworV/RtGkus4tgF8KUK7mtpodWNSW3cWmcDyBB/E3rJXfTV2wcANKiWMDrwIHi40H40pcKv5Vsaqqsaeu4ccHMCalBwAuSXB+v/IBXa6t3tUaJgFeSNDe7HmwrV0o33+yyt/L/Hamvd6VhCxe6AAJljAhbruR21qzKVkmstuQ2Lo3zASRIfzd9BLwApFl/174wPPyw9G//5h07/3zpiSfcDQtQJf4WQZLr47V4cd/2kiWFrSoIeCWLMS6rZs4c129o0yaXlTJ6tGv6XUkfJKAauZLblpbyg14XXlhdFmKucX4QZY3VNM4HkCC1vOkDgLio1bVv507ps5/1jo0c6b4hJdiFgNDDC5IKG9e3t3u3J02SjjyydvNBsJqapNNPl8491/0m2IWo5Epuy11R9JFHpHvuqfy8cWicDyBhit300bgeQNo1NBSOhRHwWrhQ2rLFO3bTTdLJJwd/LmQWAS9IciWNAyG7C0BQciW3y5dLl1/uMq/y1ddLH/hAYdXQn/yJ9MorlZ836sb5ABKmWOkiGV4A0q4WGV533y399Kfesfe/X7ruumDPg8wj4AVJ0uTJ0ogR/T//0kvu5jTsFWkBZEOu5Pbee11G+3PPSU8+6X7v2CE9+KB0223e1+zeLV1xhVsorRJz5rhrXTWqaZwPIGFqleUAAHFSV1dYUhjkKo1tbYXfHh5+uHT77TQWRuAojoVaW92Kfvv29b/P889LF13kypAWLWJFPwDBaWoqXiL4qU9Jy5a5ZvU5Tz0l/T//j/Sd75R/nieeKCzXLke1jfMBJIwxLuiVf6NHwAtAFgwbJh061Lc90LVv5sy3P2ANt1aX9O47fNiw4h+atm8vfryPfERavbqaWQMFCHhl3LJl5TWQXr/erbK2dKnrxQMAYfrXf5V+9zvpxRf7xm68UXrve6VLLin9OK2t0oc/XPkXlI2N7rpHsB/ImGHDCHgByJ5hw7w3iANd+9rb3epYkoykAYqG+rdz58DlRkCFKGnMsNbWylZL6+x0r2ttDWdeAJAzapRrVu+vLFqwQNq6tW+7o8OVQz71lPvd0dH33AsvuLYQ+WOSNGZMaXOYNs012ifID2SQv5cNTesBZIH/2kewHwlFwCujrHU3jOUGu3I6O6WrrqKnF4DwveMdLqsr39at0mc+Iz3+uGt8f/jhruT6vPPc78MPl+bNc8Gyiy+Wtm3zvv6jH5XeeGPgxvnz5rnn16whswvILH/jem76AGQBAS+kBCWNGbVihStPrMa6ddLKla4RNACE6S/+QnrsMemXv+wbW7bM/RTT3S0tXux+/C66yAXChg511685c1z216ZNrjH+6NHSxInF+4oByBhu+gBkEdc+pAQBr4y65ZbgjkPAC0DYjJHuvFOaMUPavLny45x7rnT//YVJG/01zgeQceXc9I0b1/d4/3635Kz/eX866WDHAYAoEPBCShDwyqCODtd8OQhLlrjjcaMIIGxHHil97WvSdddV9npjpG9/22VwAUBJyunhlb+62BVXuFTSnAsvdEvFAkASEPBCStDDK4Pa2ly5TxC6u99elAMAQmWt9C//Ut3rr7+e3oMAylDJTV9np/Tzn3vHrrgiuDkBQNgIeCElCHhl0J49wR5v9+5gjwcAxQTZexAASlJJ0/qf/1zau7dve8gQtwoGACQFAS+kBAGvDBo1KtjjUR4EoBaC7D0IACWp5Kbv7ru92xdfLB11VHBzAoCwNTR4twl4IaEIeGXQpEml9UwtRX29W80MAMIURu9BABhUuQGvnTulhx/2jlHOCCBpyPBCShDwyqCmJqmlJZhjtbTQsB5A+Og9CCAS5TStl1xE/eBB7+uD+tAFALXiv/Z1dUUzD6BKBLwyauHCeB0HAAZC70EAkSg3y8FfznjppXwzCCB5yPBCShDwyqg5c6SpU6s7xrRp0uzZgUwHAAZE70EgWsaYocaYq40xvzLGbDTGHDDGbDfGrDXG3GaMSWdX9nKa1m/ZIi1f7h2jnBFAEhHwQkrURz0BRMMYadEiadYst3p2uRobpbvucscBgLDleg8GUdZI70GgPMaYMyX9l6RpvqeO6P2ZJulySffWeGrhK+em72c/k6zt2x492mV4AUDSEPBCSpDhlWHNza4JdGNjea9rbHSva24OZ14A4EfvQSAavcGu5XJBrS5J/yHpY5JmSrpY0uck/URSOpeCKOemz1/O+LGPSSNGBD8nAAgbAS+kBAGvjJs7V1q1qvTyxmnT3P5z54Y7LwDwo/cgUFvGmOFyWVuHS9oi6Wxr7XXW2gestf9jrX3cWnuHtfZTkk6KdLJhKbVp/SuvSE8+6R2jnBFAUpUT8ApqVSEgBAS8oOZmae1a13bi8std2VC++npp3jz3/Jo1ZHYBiAa9B4Gau0HSKb2Pr7TWrutvR2ttOpfwKvWm7557vNtHHCFdfHE4cwKAsJV67Tt4UNqxI/z5ABWihxckuV5cc+a4n44OadMmt4rZ6NGu1w3lPwCiRu9BoHaMMUMkXde7ucJauyLC6USn1Kb1/nLGefOkoUPDmRMAhK3UgNfXvuaCXnkODRumg6NGafiwYTLlfOgaN67MSQKDI+CFAk1NBLgAxFOu92BLS3lBL3oPAmW7QFJueYe3m9EbY0ZKmiCpU9JWa21PBHOrnVJu+tatcz/5KGcEkGSlXPsef1z6znc8Qz0nnaSHv/lNdY8YoUsuuUQj6GOIiBHwAgAkSq734IIF0vr1g+8/bZrL7CLYBZTl/LzHvzPGnCvpm3KN6nMtMXYaY5ZK+qa19rVKTmKMmTTILm9/5X/gwAHt27evktNUbIgxasjb7t63T12+OdQvWqT8XK6eiRN14OyzpRrPNan25/VF299fjzQkHu9zsgx67du2TcM//WmZvJVp7dCh2nPrrep+6y1JvM9pdSBhCxgQ8AIAJE6u9+DKldLNN7vsrfyeqfX1Lgts4ULXs4syRqBsZ+Q9Pl/SD1T4ufFwuVUaP26M+ai1dlUF53m91B2ffPJJvfzyyxWconKTX3pJ78jb7njjDa169NG+AWt1sS/g9crMmVr/2GO1mmKqrFpVyV8hJA3vc/xN3rCh/2uftTrvH/5BI9rbPa9Z/+lP6+XeYJfE+5xW27dvj3oKZSHgBQBIJHoPAqE6PO/x9yQNkXSjpP+Q1CbpWLkeX1+UNEbSEmPMDGvtplpPNEw9vj5cdb5eNWM3bFDj1q2esbZ3vzv0eQFAmAa69p3wq19p3OrVnue3Njfr5Q9/uCZzA8pBwAsAkHj0HgQC15j3eJikL1lrb8obe1nSl4wxOyV9S9IRkv5W0p+VeZ5jB3l+nKSnJem8887TiSeeWObhq1O3d69ne3RDgy655JK3t4cuW+Z5vuekk3Ten/4paaVl2L9//9uZILNmzdJw/0IBSAXe53ga9q53yfiC9pIKSrKb2tr0kYULXYP6bds8z9m6Oh118KAuef/7eZ8zoNaZ1tUi4AUAAJBQxph6SQcH3XFw11hrf5S3nRYEEL0AACAASURBVN98pU0uy6uY70r6c0njJc03xvy5tXlNXQZhrW0b6Pn8Fb6GDRtW+wbIo0d7Nuu6uvrm0N0t3Xef9/krr9SIkSNrNbvUGT58OE2uM4D3OUbeeEPavHnQ3cyhQ/3uZ3p6ZHbuLHhPeZ/TaZh/QYOYqxt8FwAAAGTM7rzHy6y13cV2stYekvTr3s0jJE0Je2I1NdBKZStXSr4eNqzOCABAfJDhBQAAkFDW2kPGmNMDONQW33Z+M/kBs7B8+x4t6ZUA5hMP/oBX/qpjd9/tfe6ss6TTTgt/TgAAoCQEvAAAABLMWvuHEA67Pu/xkEH2zX/+UAhziU5/GV5dXQXljGR3AQAQL5Q0AgAAwC9/PfnBOsXnP5+qVRrlb7icC3g98oj05pve5+bPr82cAABASQh4AQAAwMNa+6qk3/duvs8YU7QTuzFmtKS5vZsvW2v9pZHJ5s/w6umRDh0qLGd817ukyZNrNy8AADAoAl4AAAAo5v/r/T1G0j/2s8/3JOWWMvz30GdUa8VWo9q5U3rgAe8Y5YwAAMQOPbwAAABQwFr7M2PMVZI+KOk6Y8yxkn4o16R+sqTrJL2/d/ffS/rXSCYapmIBr8WLpb17+7aHDJHmzavdnAAAQEkIeAEAAKA/n5R0n6RLJF3a++P3tKSPWGv3F3ku2YoFvH70I+/2e98rHX10TaYDAABKR0kjAAAAirLW7rHWvk/SFZIeltQu6aCkbZKWSfqspHdaa9ujm2WI/E3rJenpp73blDMCABBLZHgBAABgQNbaeyTdE/U8aq5Yhpf/+ZaW2swFAACUhYAXAAAAsm3mTKm9giQ1Y6SpU/u2x42TVq8Obl4AAKBiBLwAAACQbe3t0qZN5b9u//7KXgcAAEJHDy8AAAAAAACkCgEvAAAAAAAApAoBLwAAAAAAAKQKAS8AAAAAAACkCk3rAQAAAADImnHj4nUcIGA1C3gZYyZL+gtJl0qaLOmApJck/UzSLdbavQGc40xJfyZpjqSJkrolvS7pV5L+1Vq7sYxjNUq6WtJlkk6TdKSkXZI2SfpvSb+w1j5a7ZwBAAAAAKi51aujngEQqpoEvIwxl0r6L0mH5Q2PlHRO78/njTEftNa+UsU5/l7S30kyvqfO6P35gjHmamvt0hKO9R5Jd0o6zvfU0b0/75D0bkkEvAAAAAAAAGIm9ICXMWaGXBbXSEl7JH1b0nJJIyTNl3StpFMl/coYc461dk8F5/gbSV/r3dwi6buSftu7fYGkL0saJ+luY8xF1trfFh7l7WNdLOkXkoZL2i3ph5Iel7RV0lGSjpf0AUnHlDtPAAAAAAAAhK8WGV7flwt2HZJ0ibX2d3nP/doYs0HSjXJlg38t6f+Uc3BjzERJ3+jd3CzpHGvt5rxdnjTG/EzSU5ImSLrFGNNsre0pcqyjJN0jF+x6vne+bUVO+x/GmIZy5gkAAAAAAIDaCHWVRmPMOXL9tCTpdl+wK+cf5YJLkvSXxpihZZ5mvqRhvY+/7gt2SZKstZskfb13c4ZchlYx35Z0hFx/sZZ+gl25Y3aVOU8AAAAAAADUQKgBL0kfy3t8Z7EdejOtFvVujlVfgKxU5+Q9fmiA/R7Oe3y5/0ljzBhJV/Zu3m2tfaHMeQAAAAAAACAGwg54vbv3d6ek/xlgv5V5jy8s8xyH5z3eOsB++c/NLvL8h+X6iknSvblBY8xoY8zJxpijy5wXAAAAAAAAIhB2D6/Te3+/ZK09NMB+fyjymlJ15j0+TNKOfvbLXyHyeGPMSGvt3ryx8/Me/84Y8365VR8vyA0aY7bI9fj6lrV2e5nzlDFm0iC7TMw92LJlS7mHBwAAAfP9ezwkqnlk2Nt/5u3t7RoxYsRA+1auuzu447T12xEDRezbt0/bt7uP1W1tbeG9x4gU73M28D6nX3t7e/5m7D8XhRbwMsYMl3Rk7+aA//Jba980xnRKapR0bJmnel59pZOzJS3pZ79Z+dOTNEnSi3ljZ/T+fkvS5+Ua6fuNl/RXkuYZY95nrX2uzLm+XuqO5557bpmHBgAAITtK0mtRTyJjjso9mDVr1kD7xUN7u3RsuR9lAQBIpNh/LgqzpHF03uM9Jeyfy9QaVeZ5Hsh7/LXeQJtH79jXfMOjfdu50sgRkr4j17j+b+QCY8MkTVNfr7FJkh4wxpQ7VwAAkFy0N6g9/swBAIin2P8bHWZJY37gqZQVDQ/0/i4r79Fa+6Qx5ueSPiK3AuNKY8xXJeVWhHynpG/1PtclqaGf8zT2/s49/xlr7b15z6+XdJUxZr+kP5F0kqTrJN1UxnQH+8pvsqT/7n18vqRNZRwbyTFO0tO9j8+R1D7Avkgm3uNs4H3OhomS/m/v4z8MtCNCkf9nzmejdOJamg28z9nA+5x+ifpcFGbAa3/e44Z+9+ozrPf3vgrOdZXcCo3nSzpX0mNF9nla0jpJ1/Ru7/Y9nz/f/+sLduX7au/5hkm6QmUEvKy1A5Z2GmPyNzcNtj+Syfc+t/M+pw/vcTbwPmeD730u5Qs8BCv/z5zPRinEtTQbeJ+zgfc5/ZL2uSjMksb8gFIppX+5DKtSyh89rLW75Pp3/ZUkf1+tdknflFsxsilv/E3ffvnzfWiAc+2QtLp3c4YxZmi58wUAAAAAAEB4Qgt4WWv3S8qtZDjg6oTGmLHqC3iV3Njdd74ua+33rbVTJY2RdIqkCZImWGu/Zq09IOnM3t13q7CRfv55B4tE5/YdIumISuYLAAAAAACAcISZ4SW5FRQl6SRjzEDlk6cVeU3FrLVvWWs3WGu3WGutJBljjpHruyVJT1tre3wvW5/3eLDlNfOfP1TdbAEAAAAAABCksANev+n93Sjp7AH2m533+L/73as6V0jKFZz+rMjzq/IenzjIsXLP75O0s8p5AQAAAAAAIEBhB7zuz3t8TbEdjDF1khb0bu6StDzoSRhjmiR9Je8cPymy2ypJ23off8z4urHlHWuKpLN6N39bJFMMAAAAAAAAEQo14GWtfUrSE72bnzPGvLPIbl+UdHrv43+21h7Mf9IYc7Uxxvb+fKPYeYwx4/trHm+MGS3pPrklUiXpBmutf4VGWWu71bfi4qnqC5DlH2uopFvU9+f278XOCQAAAAAAgOgM1FcrKNfLlSmOkPSoMeZbcllcIyTNl/Qnvfu9KOkfKzzHpyTdYIy5S9JKSVvkVmQ8X9JCSZN797vTWnv7AMf5gaRPSmqW9G1jzFRJP5bL/DpJ0l9LOq933wflAmkAAAAAAACIEdPb0z3ckxjzYbnAUVM/u7wo6VJr7UtFXnu1pDt7N//eWvuNIvvcIOm7A0zhkFww7auDlSAaY8ZL+oUG7jn2oKT5xTLFAAAAAAAAEK1aZHjJWvsLY8yZctlel0qaJKlL0kuS7pX0r9bavVWcYomk4ZIukmsof7SkA5LaJD0q6XZr7fr+X+6Z6xZjzPmSPifX6P4MSWMk7ZD0lKQfWWuXVjFXAAAAAAAAhKgmGV4AAAAAAABArYS9SiMAAAAAAABQUwS8AAAAAAAAkCoEvAAAAAAAAJAqBLwAAAAAAACQKgS8AAAAAAAAkCoEvAAAAAAAAJAqBLwAAAAAAACQKgS8AAAAAAAAkCoEvEJgjJlsjLnJGPO8MabTGLPTGPOUMeYGY8zIKo9db4x5hzHmC8aY24wxa4wxh4wxtvfn+GD+KzCYkN/nmcaYLxpj7ul9j7cYYw4YY3YbY14wxtxljHlPUP8tKC7k9/gbef/fDvYzJ6D/JBQR1vtsjJlTxnuc+1kR4H8aeoX5/3LeOc40xvzQGPNi7zk6jDHrjTE3GmMmB3GOJOOzUfrxuSgb+GyUfnwuyobMfDay1vIT4I+kSyXtkmT7+fmDpBOqOP7XBzi2lXR81H8GWfipwfv8/7d37+GSVeWdx78/LkJzx3ARgbG5GEW5KAiK4SZgUIkyBBREpBuJgsP4OBk0opn0NJrITIw+EMlkUJTmOoAoiRhB1IcmXCIihItgAgRaELlf5dZA884faxW1+3TVrjp1alfV2fv3eZ711N61V621a79Vdd6zdtXaV/WIc6tcAKw+7uNRxzKCGC/sM8YB7DXu41HXUmWcgb2mEeNWOXXcx6Ruper3cu7jBODlkj6eBA4c97GoawycG42/OC9qRnFuVP/ivKgZpUm5kfLO2BBI2gG4BlgDeBo4EbgcmAMcCnw8V/03YOeIeHqAPhaSEjuA54EbgQ2BrfJ9W0TEksGegfVjRHH+KbBy7udXwAPAo6RY7wAcA2yRq58fEYcO+nxsRWN4L2/Xo/rdEfHMdPuwclXHWdKatN+nZU4B9szLfxAR10ynH+tuRO/l43O7APcDX8l9ArwT+DPgNcBSYO+mxde5Uf05L2oG50b157yoGRqXG417dLFOhfRCCeBFYNcO2z9Le0RzwYB97AccDewIrJLvW1Rod+64j0Pdy4jivEqP7XNIHxqtfrYb93GpUxlRjBe22hj3821qGUWc+9iH9Uj/oAdwx7iPSd1K1TEGNi3E7z7gtV3q3Jfr3AisNO7jUqcY5DacG9U/xs6LmhFn50Y1j3Ef++C8aJbHedJyo7Ef8LoUYOfCC+P/dqmzEnBbrvMYsOqQ+nZS14A4d+jnkMK+HDvuY1OXMqoYO6lrRpz72I+jq04em1pGEWPguEIff1JS708K9fYf97GpUwxK+nZuVPMYd+jHedEsj7Nzo/rHuI/9cF40y+M8abmRJ60fnv9cWD69U4WIeBk4M6+uT/ods80ukxTn4te4V6+ojyaapBhbdSYlzke0ugPOqqD9JhtFjHcuLF9SUu/SwvLB0+xjNpuU95lVZ5Ji7LyoOpMUZ6vGpMTYeVG1GpcbecBreHbPt88A15fUu6KwvFt1u2MVmaQ4f7iw/G8V9dFEkxRjq87Y4yxpK9I8BgBXRsTdw2zfRhLjVxeWHyypV9y25zT7mM3G/j6zyk1SjJ0XVWeS4mzVGHuMnReNRONyIw94Dc82+fbOiHippF7xD/A2XWvZpBpbnCWtJGljSXtLugg4LG/6d+BHw+jDgDHEWNKPJT0q6QVJD0laLOl4SevPpF0rNQmf2UcUls8Ycts2mhgXv1Gybkm94ra5w7rc9ywwCe8zq5bzomZwblR/k/B57byoeo3LjTzgNQSSVgc2yKu/KasbEY/TfhFsXuV+2XCNK86SlkgKYBnpqkQ/pf111F8DB/X4wLI+jfG9vC/pbMiqpCtO7Um6ssldkg6YYds2xQR9Zh+eb58DLhxy2402whj/qrBcdnZyj+LuAZtNs59ZZ4LeZ1YR50XN4Nyo/ibo89p5UYWamht5wGs41i4s93PZztaLZ60K9sWqM0lxfglYAGwfEbdW0H5TjTrGtwBfAt4P7AS8A5gHXJa3rwd8V9J7B2zfOhv7e1nS7sCWefWiiHhqWG0bMLoY/2NheUFOJpeT71sw5e61p9arobG/z6xykxRj50XVcW5Uf2N/LzsvGolG5kYe8BqOYhBf6KP+0nw7p4J9seqMK85/CGwH7ADsDfwF8BDwP4BTJPmfg+EZZYxPiojtI2JBRPwgIm6IiGsj4syI2A84JtdbGThNkj8vhmcSPrM/Wlg+s2stG9RIYhwR1wLfz6s7AFdI2kfSGrnsQ5oHY4cp+9GE9/MkvM+sWs6LmsG5Uf1Nwue186LqNTI38oDXcDxfWH5VH/VXy7fPVbAvVp2xxDkibo+IX0bEzRFxeUT8JbAtcBPpj8PVkprwbYFRGFmMI+KJHttPBU7Lq68F/ni6fVhXY/3MlrQa8MG8+lvgJ8No15YzyhjPA36Wl3chxfOZXH6S77sOOKfwmN8N0M9s49yo/pwXNYNzo/pzXtQMjcyNPOA1HMXg9HNWac18289XCW1yTEyc8++q5+XV7YHPD7uPhpqYGGenFpabdGW3qo07zgeQfpIBcE5ELBtSu9Y2shjnf9D2BP4UuG3K5gdIP83ZHVincP/j0+1nFhr3+8yqNzExdl5UqYmJc+bcaPjGHWPnRaPRyNzIA15DEBHPA4/k1dLJ1vKVRVovnnur3C8brkmLc0T8Crgjrx5cRR9NM2kxZvk/EJtW1EfjTECci1ch8tf2KzDqGEfECxFxUkS8mZS0/z7p2wevzT/NWUr6JxxSwlk6WWwdTMD7zCo2aTF2XlSNSYszzo2GbgJi7LxoBJqaG3nAa3haVyPYWtIqJfXe2OExNntMWpwfzrevq7CPppmkGKuidm1McZa0EbBfXr0hIn450zatq7HEOCKejIg7IuL+iAgASRsDW+cq10XEyzPtZ5aYpM9Tq8akxdh5UTUmKc7OjarhvKgZGpcbecBreK7Kt2uSrijSTfGrt1dXtztWkUmLc+vMln8CMjyTFOM3FZZ/W1EfTTWuOB8GtBIMn8Ws1iS9lz9M+5+0CyrqYxJNUgysGpMWY+dF1ZikODs3qobzomaYpPfySHIjD3gNzz8Ulo/sVEHSSrS/svkEcHnVO2VDNzFxlrQz7TOYt1TRR0NNTIyBowvLV1TUR1ONK86t9l4Czh1Ce9bdRLyXJa0DfK7QR5PiPhExsEpNTIydF1VqYuKMc6OqOC9qhol4L48yN/KA15BExM+BK/PqUZJ27VDtOGCbvHxyRLxY3ChpvqTIZWF1e2uDGkWcJe0iacey/ZC0KXBG4a6z+n0OVm5EMd5O0tZT759S52jgqLz6AHDRNJ6G9TCOz2xJbwbemlcviYiHy+rbzIwqxpI2kbRql21rA98FXpPv+kxENOEKjYBzoyZwXtQMzo3qz3lRMzQxNyr73aZN36dJX/mbA1wm6cukEdE5wKHAJ3K924GvDtKBpLVYcSLO4h+HgyU9Uli/MSJuHKQv66rqOL8JOF3SNcDFwI2056TYFHgXaUR+3XzfT4DTB+jHuqs6xjsBp0m6HLiEdCb6UdJn8huBw4F357rLgKMj4pnBnoqVqPwze4p5heUzutayYRpFjD8CfEbSGaRvG9xPuurQO4D/AvynXO/0iPjWgH3MZs6N6s95UTM4N6o/50XN0KjcyANeQxQR/yrpEOBsUkC/3KHa7cD+MxjF3IDyP+JfmbJ+AikxsCEZUZwB3plLmUXAsQ2aAHkkRhTjlYF9c+nmUeCoiPj+gH1YiRG+l1tfDz8srz4O/GAm7Vl/RhjjjYE/y2Wql0gJ4xdm0P6s5dyo/pwXNYNzo/pzXtQMTcuNPOA1ZBFxsaTtSSOn+5Mu+fkCcCfwHeCUiHh2jLtoQ1BxnM8nTcK5Nymx2xTYCHgV8BTpkttXA2dFxM0zeR7WXcUx/iHpK/m7kr7KvTHwe6SJGx8DbgIuBRZFxFMzeR5WboSf2fvQnkz5/HwpZhuBEcT4e8DqpM/srUif10tJl9e+DPhWRNw6g/ZnPedG9ee8qBmcG9Wf86JmaFJupHxVSDMzMzMzMzMzs1rwpPVmZmZmZmZmZlYrHvAyMzMzMzMzM7Na8YCXmZmZmZmZmZnVige8zMzMzMzMzMysVjzgZWZmZmZmZmZmteIBLzMzMzMzMzMzqxUPeJmZmZmZmZmZWa14wMvMzMzMzMzMzGrFA15mZmZmZmZmZlYrHvAyMzMzMzMzM7Na8YCXmZmZmZmZmZnVige8zMzMzMzMzMysVjzgZWZmZmZmZmZmteIBLzMzMzMzMzMzqxUPeJmZmZmZmZmZWa14wMvMzMzMzMzMzGrFA15mNlSS5kuKXOaOe39GRdLC1vMe975Yfwqv04Xj3hczM6sv50bOjWYL50ZWNx7wMjMzMzMzMzOzWvGAl5n1JGmvwhmfvca9P03lOJiZmU0G/02eDI6DmZXxgJeZDVVELIoI5bJk3PtjZmZmNk7OjczMxsMDXmZmZmZmZmZmVise8DIzMzMzMzMzs1rxgJfZGEjaQNJXJN0u6TlJD0r6saQD8/bSq/lIWpK3LcrrO0laJOluSUunXg1H0paSjpN0cX7sc7n8WtL5kt7TZT/n5rYuL9x9eWHfWmV+4TG99n1x3rY4r28q6WuS7sz79KikH0l6bx/HUZLmSfpnSY9LelrSLZIWSFon1xnK1WYkbSbp7yTdJel5Sb+V9H1J+/bx2LmdjlWXusvFtvh4+oiDpFUlPZDXL+lj37YtPP4LveqXtLNhPu5XS3oovw7vzesLJL2hUPcDhT4P6aPtr+a6yyRtXvI8vp7j/7ikZ/Nr6lJJn5S04Qye2xsk/a2kWyU9mV+nd0k6XdKOg7ZrZmZtzo2cG5XUdW60YtvOjcz6EREuLi4jLMAOwENAdCmnAvML63M7tLEkb1sEHAO8OLWdQt0tSvoqlrOAVab0M7fPx84vPKbXvi/O2xYDuwGPlLT7mZLj+Crg4pLH3g68rrC+cAYx2xN4sqSvBcDCqce+y3Gc36OvV2I7aByAv87ry4BNe/T3tVz3pV51S9r4CPB0j31bUqi/MnBfvv/SHm2vAjzYrW5u62v5uZb1v6jDY3u+NoC/oMP7q1BeBk4Y9+eKi4uLy2wuODdajHOjsr5eie2gccC5kXMjl0aWVTCzkZG0PnAp0Dqjcg5wNvAwsDXwaeATpMSvHzsDhwP3An8DXE/6I7d7oc7KwAvAj4AfA7cBjwGvBn4fOBZ4c27nLuB/Fh57H7Bd7ufb+b6PAddN2Y/f9Lm/RZsAF5H+GB8PXJX3czdSkrQecKKkSyLi1g6P/zrwR3n5NtLzvwVYBzgQ+CRw3gD7tZx8JvZiYG3SH/BvABeSkrzt876fAPxipn2VmG4cTgM+S/oW7xHAiZ0albQqKe4Al0XEfdPdMUlHAGfk1eeBbwKXAA8Aa5GO0fuB17ceExHLJJ0BfB54t6TNIqLba2h/YKO8/O0O279BOhYA9wOnANeQ4rMhsAtw8HSfV35uXyQldeQ2vw3cSkry3gD8V2BXYIGkRyLi64P0Y2bWZM6NluPcqH/OjRLnRmZlxj3i5uLSpAKcTPvsx3Edtq8M/APLnyWZ26HeksL2m4H1SvpcE9ikZLuA03NbTwPrdqizV6G/vXo8x/k99n1xYfsSOpw5IyV2L+c6J3fYvmNh+7XAGh3qHDzlOC4cMGbfLbTx4Q7b1wZuLPbVoc7cwvb5PfprxXbRDONwRa53e0mdAwvtHTTAsXkt8Ex+/IPAtiV1N5uyvmUhhl8oedw/5jqPAKtN2XZAYf+v6fE+2KzDfV1fG6QEunVm9Etd2lyJdPY/gKfK+ndxcXFx6VxwbgTOjeb36K8V20UzjINzo5L+833OjVxqVTyHl9mISFodmJdXbyB91Xg5EbEMOJp0Nqhfx0bEE902RsQzEXF/yfYAjiP9AVsT6DnvwhB9KjqcOYuIq0jJGix/RrblE6RkFODjEfFshzYuJJ0lHZikTUiJA8APIuL/dejnd3l/Js1p+fb1kv6gS50j8+0jpDO10/UpYI28fHRE/LJbxZhyljIi7qI978b8To+RtBHwvrx6TkQsnVLl+Hz7LPDBHu+D6Z5p/xwpabuedFa9U5svk47BUlJyP9DZUjOzpnJu1JFzo+o4Nyrpvw/OjWzW8YCX2ejsBKybl8/MydQKIuJB0lfs+3FvRFw5nZ3IE3duJmmbPJnltqSzUY/mKv3+ZGCmngD+qWT79fl2yw7b9sm3N0bEzSVtnDnIjhW8i3RmGdKZ3o4i4uekr3NPkgtJxxjaydsrJG0MtCa/PTsiXhigj/3z7d2ks43TVUw8d+uw/aPwyk/vl/vKvqTfA96eVy/o9M/BoPLPGVrH5sJu71WAnEjekld3HdY+mJk1hHOj5Tk3qpZzowE5N7LZygNeZqOzbWH5+q61kn7nPChLaF6RE7ljJf2M9NX8e0lzO9xSKK25ADbos++ZuiOfBermsXy7dvHOfDZ467w6rOPYzXaF5alzQkz18xn2NVQR8Rxwbl79kKQ1p1TpmjD1Iyc+rdf0lWWJT4nv0Y7zColn4b4bIuKmKdveQvtM9j8P0HeZN9E+O3tihys+LVeAt+W6rxnyfpiZ1Z1zo+U5N6qQc6MZcW5ks5IHvMxGZ/3C8kM96j7cZ5uP96og6dXAv5AmrHw76Qo+Zeb02fdMrfBV+ylaCd/Uz6n1CsvDOo7dTCdmD86wryp8M9+uDRw0ZVsrYbouIm5h+l5NO6nq+rOQMvlr+Gfl1eUST0m7kCYMBvhWh4cX//kYqP8SG/Wu0tEavauYmVmBc6PlOTeqnnOjwTg3slnJV2k0m92W9VHnZNJPBiBN+vpt0tnPh4DnW2efJN0DbE77D3UdDHJmrah4LHq1NXHHLSJulHQ9Kf5Hkn/GIOntpDN1MMAZzE5dzeCxp5GuwLUWaZ6H1lWNWknn87TPxlbRfycrF5Y/S7p6WD+eGfJ+mJnZ9Dk3KufcyLnRIJwb2azkAS+z0SmecdwIuL2k7oYl2/omaR3gkLx6bkR8pKT6+iXbJklx8s1eZ5sGPRvV8lhheWPSzx0G6av484Re36yd+vX6mTqNlNTtKWnLPCFqK2F6Dlhhstk+PUZ6XiuR5jkZSET8UtK1pDPsRwJn5J9mHJqrXNRlwtVHCssD99/Fo4XlVcsmnDUzsxlxbjQczo2mx7nR9Dk3slnJP2k0G53ixJ1v61qrv+39ej2wal4+r1slSW8gnUXqZthniQYWEc8D/5FXqz6Oxa+z79yjbtn23xWWuybPebLRsnlCBonDuaSfSAiYJ2kO7YTpexHx5ABtEhEvAq1kZ3dJMzmL25qgdQ9JWwJ/TPvnGd3Osv4r7eOxxwz67uRWoDVR7R8OuW0zM2tzbjQEzo2mzbnR9Dk3slnJA15mo/MLoPUH9KPd/gjmK8TsN6Q+i9/iLPsN/TE92ileCny1wXdnaH6ab3eQtH1JvSNm2M/ltH8aMa9bJUlvY/mJd5cTEY/TPvtalmh+uMf+TDsOEfEUcEFenUf6anzrilid5n+YjtbluregfYnyQZxHmjBYpMtwt86yLqEd6+VExGPAcW28igAABrFJREFUNXn1Q5KGdiYzX8q91e9eec4MMzMbPudGw+PcyLmRcyOzKTzgZTYi+exb61LQOwL/fWodSSsBpwKrD6nbO2mf6emY4Ej6I+BTPdopTny51RD2a6a+Qft5fVPSCgmrpIOAA2fSSUTcT/uS0h+Q9KEO/ayV96eX1tVyDpC0wjGUtA3wxR5tDBqH1lnC1wF/nZfvBhZPo41OTqE9N8Op+TLuHUnarNu2iHia9ln2o4G98/KiHlc4+t/5dg3gO5LW7VaxrP8u/or2a+y8TjErtL2ypMMG6MPMrNGcGw2VcyPnRuDcyGw5HvAyG62FwAN5+W8knS1pP0k75oThStLZoOJlnAf+ynxEPAr8MK++T9Klkg6UtJOk90o6jTRZ612UXLUnIu4BfpNXPyPpAElvlLR1Lmt3e2wVIuJ62lfZ2QW4TtL8/LzeJelvgfMZznE8jvbX7s+V9He5j50kHUm6/Pdb6X2Z7/+Tb+cAiyUdleO+h6QvAj8jzY8w9DhExNXAr/Jq6/LQpw94uexiuw8An8yrGwE/l3SypPdIeouk3SQdI+mHwBU9mmslnhuR/jYFsKhH/xfTPhP7TuA2SZ/Px/QtkvaVdLykG4C/nOZzu5p2kr0FcKOkkyS9T9JbJb1D0qGSTgbuAc5h+atkmZlZfxbi3GjGnBsBzo2cG5lNFREuLi4jLMAOpKsARZdyOvCxwvrGHdpYkrct6qO/zYFfl/T3a9JVaUrbJP3x7tbG/EK9+YX753ZoZ3HetrjHfi9stdNl+6tIXxvvtk93kc70tdY/N4OY7QU8VdLXwl77m9s5uaSNe4YZhw6PO65Qbxmw+RBf0/NIc2F0268AlvTRzs2F+pf12ffKwNdJk8SW9b/C8SzGr6T9/0b6uURZ2wEsBbYe1jF1cXFxaVLBudFinBs5N+rcjnMjF5cZFH/Dy2zEIuIm0h/vrwJ3kP4YPEKaE+GwiDgSWKfwkIEmziz0dy/pZwJfIV39aGlu8ybgBOAtEXFbH+38PXAQcBkpKX1pJvs1UxHxAvAB0pwGV5Ge07Oks3VfJl19p3hFmYGPY0QsBt4M/D0pCX4BeBD4J+A9EbGwz3Y+DRxG+gr/U6QrAf078L+At1Ych7MKyz/Or4uhiIgzSAn0X5HO6j5BOkb3kGLz58C7+mjq7MJyX5cEj4hlEfEp0vwf3yC9xp8hvRbuIJ3F/zjwp/2016H9k0jP7UukM82PkI75M7mv75Lmedk0Iu4cpA8zs6ZzbjQczo2cG+W+nRuZZYqIce+DmU2Rv05/FPCbiNh83PszW0najfRTCIB9I6LjJJ9NIGkf4Cd59ZCIuKCs/jhIOgs4nHSZ+k0iYumYd8nMzCaEc6PhcG7U5tzIrP78DS+zCaN0aeTWVV1+Ns59qYHWlX1eJJ1da7KP5dtHaU82OzHypKoH5dVznNCZmVmLc6Ohcm7U5tzIrOY84GU2YpK2krpedntl0lfDN8h3nTGyHZtlJG0gqetEmJL2I13VBuD7EfFEt7p1J2ku8MG8evqEJkzHkiathXQ1LjMzawjnRsPh3Kh/zo3MmsE/aTQbMUmLSFfPOQ+4ljTXwBxge9Lv6XfMVX8KvDv8Ju1I0l6ks3HfIX0d/T9Ik3O+jjR/xeGkSTufI83Fcft49nQ8JG1KuiT1FuR5MEgTjG4dEfeNc98AJK0CzAVWA/YgzdsyB/hBRLx/jLtmZmYj5txoOJwblXNuZNY8q4x7B8waahvSpKjdXE2aS8AJXbl1SPN5HNVl+1PAB5uW0GXnAHtOuW/BJCR02WakiVOLnmLACVTNzGzWc240HM6NunNuZNYwHvAyG70TSVcweTfpjNuGwKqk+QN+AZwPnBcRL49tD2eHX5Au8/1e0hngDYH1SInBncClwCkR8fC4dnBCPEt6vZ2Urxg0iR4C/gX4c1/Nx8yskZwbDYdzo/44NzJrCP+k0czMzMzMzMzMasWT1puZmZmZmZmZWa14wMvMzMzMzMzMzGrFA15mZmZmZmZmZlYrHvAyMzMzMzMzM7Na8YCXmZmZmZmZmZnVige8zMzMzMzMzMysVjzgZWZmZmZmZmZmteIBLzMzMzMzMzMzqxUPeJmZmZmZmZmZWa14wMvMzMzMzMzMzGrFA15mZmZmZmZmZlYrHvAyMzMzMzMzM7Na8YCXmZmZmZmZmZnVige8zMzMzMzMzMysVjzgZWZmZmZmZmZmteIBLzMzMzMzMzMzqxUPeJmZmZmZmZmZWa14wMvMzMzMzMzMzGrFA15mZmZmZmZmZlYr/x8er6VbNqqabgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=200)\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(gdc, mode_tran, \"bo-\")\n",
    "plt.xlim(gdc[0], gdc[-1])\n",
    "plt.xticks([t for t in np.linspace(0.1, 0.9, 5)])\n",
    "plt.xlabel(\"grating duty cycle\")\n",
    "plt.ylim(0.96, 1.00)\n",
    "plt.yticks([t for t in np.linspace(0.96, 1.00, 5)])\n",
    "plt.title(\"transmittance\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(gdc, mode_phase, \"rs-\")\n",
    "plt.grid(True)\n",
    "plt.xlim(gdc[0], gdc[-1])\n",
    "plt.xticks([t for t in np.linspace(0.1, 0.9, 5)])\n",
    "plt.xlabel(\"grating duty cycle\")\n",
    "plt.ylim(-2 * np.pi, 0)\n",
    "plt.yticks([t for t in np.linspace(-6, 0, 7)])\n",
    "plt.title(\"phase (radians)\")\n",
    "\n",
    "plt.tight_layout(pad=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The phasemap is shown above. The left figure shows the transmittance which is nearly unity for all values of the duty cycle; the Fresnel transmittance is 0.96 for the glass-air interface. This is expected since the periodicity is subwavelength. The right figure shows the phase. There is a subregion in the middle of the plot spanning the duty-cycle range of roughly 0.16 to 0.65 in which the phase varies continuously over the full range of -2π to 0. This structural regime is used to design the supercell lens.\n",
    "\n",
    "In the second part of the calculation, the far-field energy-density profile of three supercell lens designs, comprised of 201, 401, and 801 unit cells, are computed using the quadratic formula for the local phase. Initially, this involves fitting the unit-cell phase data to a finer duty-cycle grid in order to enhance the local-phase interpolation of the supercell. This is important since as the number of unit cells in the lens increases, the local phase via the duty cycle varies more gradually from unit cell to unit cell. However, if the duty cycle becomes too gradual (i.e., less than a tenth of the pixel dimensions), the `resolution` may also need to be increased in order to improve the accuracy of [subpixel smoothing](https://meep.readthedocs.io/en/latest/Subpixel_Smoothing)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "phase-range:, 6.086174\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00169206 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 60.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-30,0)\n",
      "          size (1.8,0.109864,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-29.7,0)\n",
      "          size (1.8,0.123415,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-29.4,0)\n",
      "          size (1.8,0.136671,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-29.1,0)\n",
      "          size (1.8,0.152579,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-28.8,0)\n",
      "          size (1.8,0.169076,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-28.5,0)\n",
      "          size (1.8,0.186752,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-28.2,0)\n",
      "          size (1.8,0.0500621,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-27.9,0)\n",
      "          size (1.8,0.059489,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-27.6,0)\n",
      "          size (1.8,0.0692104,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     ...(+ 192 objects not shown)...\n",
      "time for set_epsilon = 1.79243 s\n",
      "-----------\n",
      "Meep progress: 12.530000000000001/125.0 = 10.0% done in 4.0s, 35.9s to go\n",
      "on time step 1253 (time=12.53), 0.00319322 s/step\n",
      "Meep progress: 24.92/125.0 = 19.9% done in 8.0s, 32.1s to go\n",
      "on time step 2492 (time=24.92), 0.00322858 s/step\n",
      "Meep progress: 37.88/125.0 = 30.3% done in 12.0s, 27.6s to go\n",
      "on time step 3788 (time=37.88), 0.00308826 s/step\n",
      "Meep progress: 50.44/125.0 = 40.4% done in 16.0s, 23.7s to go\n",
      "on time step 5045 (time=50.45), 0.0031845 s/step\n",
      "Meep progress: 62.02/125.0 = 49.6% done in 20.0s, 20.3s to go\n",
      "on time step 6203 (time=62.03), 0.00345439 s/step\n",
      "Meep progress: 73.87/125.0 = 59.1% done in 24.0s, 16.6s to go\n",
      "on time step 7388 (time=73.88), 0.00337615 s/step\n",
      "Meep progress: 84.91/125.0 = 67.9% done in 28.0s, 13.2s to go\n",
      "on time step 8492 (time=84.92), 0.0036253 s/step\n",
      "Meep progress: 94.47/125.0 = 75.6% done in 32.0s, 10.3s to go\n",
      "on time step 9449 (time=94.49), 0.00418097 s/step\n",
      "Meep progress: 105.32000000000001/125.0 = 84.3% done in 36.0s, 6.7s to go\n",
      "on time step 10534 (time=105.34), 0.00368885 s/step\n",
      "Meep progress: 117.42/125.0 = 93.9% done in 40.0s, 2.6s to go\n",
      "on time step 11744 (time=117.44), 0.00330608 s/step\n",
      "run 0 finished at t = 125.0 (12500 timesteps)\n",
      "get_farfields_array working on point 996 of 1000 (99% done), 0.00401804 s/point\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.00165105 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 120.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-60,0)\n",
      "          size (1.8,0.09101,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-59.7,0)\n",
      "          size (1.8,0.115166,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-59.4,0)\n",
      "          size (1.8,0.141974,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-59.1,0)\n",
      "          size (1.8,0.174379,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-58.8,0)\n",
      "          size (1.8,0.0533026,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-58.5,0)\n",
      "          size (1.8,0.0730401,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-58.2,0)\n",
      "          size (1.8,0.0933667,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-57.9,0)\n",
      "          size (1.8,0.117523,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-57.6,0)\n",
      "          size (1.8,0.143741,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     ...(+ 392 objects not shown)...\n",
      "time for set_epsilon = 3.96254 s\n",
      "-----------\n",
      "Meep progress: 5.39/125.0 = 4.3% done in 4.0s, 88.8s to go\n",
      "on time step 539 (time=5.39), 0.00742406 s/step\n",
      "Meep progress: 11.540000000000001/125.0 = 9.2% done in 8.0s, 78.7s to go\n",
      "on time step 1154 (time=11.54), 0.00651044 s/step\n",
      "Meep progress: 17.45/125.0 = 14.0% done in 12.0s, 74.0s to go\n",
      "on time step 1745 (time=17.45), 0.00677692 s/step\n",
      "Meep progress: 23.16/125.0 = 18.5% done in 16.0s, 70.4s to go\n",
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      "Meep progress: 29.25/125.0 = 23.4% done in 20.0s, 65.5s to go\n",
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      "Meep progress: 35.27/125.0 = 28.2% done in 24.0s, 61.1s to go\n",
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      "Meep progress: 40.980000000000004/125.0 = 32.8% done in 28.0s, 57.5s to go\n",
      "on time step 4098 (time=40.98), 0.00700592 s/step\n",
      "Meep progress: 46.58/125.0 = 37.3% done in 32.0s, 53.9s to go\n",
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      "Meep progress: 52.46/125.0 = 42.0% done in 36.0s, 49.8s to go\n",
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      "Meep progress: 58.76/125.0 = 47.0% done in 40.0s, 45.1s to go\n",
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      "Meep progress: 64.7/125.0 = 51.8% done in 44.0s, 41.0s to go\n",
      "on time step 6470 (time=64.7), 0.00673928 s/step\n",
      "Meep progress: 69.12/125.0 = 55.3% done in 48.0s, 38.8s to go\n",
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      "Meep progress: 73.92/125.0 = 59.1% done in 52.0s, 36.0s to go\n",
      "on time step 7392 (time=73.92), 0.00833534 s/step\n",
      "Meep progress: 79.69/125.0 = 63.8% done in 56.0s, 31.9s to go\n",
      "on time step 7969 (time=79.69), 0.00694212 s/step\n",
      "Meep progress: 85.4/125.0 = 68.3% done in 60.0s, 27.8s to go\n",
      "on time step 8540 (time=85.4), 0.00700793 s/step\n",
      "Meep progress: 91.31/125.0 = 73.0% done in 64.0s, 23.6s to go\n",
      "on time step 9131 (time=91.31), 0.0067687 s/step\n",
      "Meep progress: 96.35000000000001/125.0 = 77.1% done in 68.1s, 20.2s to go\n",
      "on time step 9635 (time=96.35), 0.00797192 s/step\n",
      "Meep progress: 99.73/125.0 = 79.8% done in 72.1s, 18.3s to go\n",
      "on time step 9973 (time=99.73), 0.0118546 s/step\n",
      "Meep progress: 104.79/125.0 = 83.8% done in 76.1s, 14.7s to go\n",
      "on time step 10479 (time=104.79), 0.00790892 s/step\n",
      "Meep progress: 109.58/125.0 = 87.7% done in 80.1s, 11.3s to go\n",
      "on time step 10958 (time=109.58), 0.00836883 s/step\n",
      "Meep progress: 114.53/125.0 = 91.6% done in 84.1s, 7.7s to go\n",
      "on time step 11453 (time=114.53), 0.00808508 s/step\n",
      "Meep progress: 120.01/125.0 = 96.0% done in 88.1s, 3.7s to go\n",
      "on time step 12001 (time=120.01), 0.0073038 s/step\n",
      "run 0 finished at t = 125.0 (12500 timesteps)\n",
      "get_farfields_array working on point 446 of 1000 (44% done), 0.00898336 s/point\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "get_farfields_array working on point 897 of 1000 (89% done), 0.00887155 s/point\n",
      "-----------\n",
      "Initializing structure...\n",
      "Padding y to even number of grid points.\n",
      "Halving computational cell along direction y\n",
      "time for choose_chunkdivision = 0.000861883 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 7.8 x 240.3 x 0 with resolution 50\n",
      "     block, center = (-2.4,0,0)\n",
      "          size (3,1e+20,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-120,0)\n",
      "          size (1.8,0.109569,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-119.7,0)\n",
      "          size (1.8,0.161122,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-119.4,0)\n",
      "          size (1.8,0.0609619,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-119.1,0)\n",
      "          size (1.8,0.0983747,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-118.8,0)\n",
      "          size (1.8,0.145804,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-118.5,0)\n",
      "          size (1.8,0.0518297,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-118.2,0)\n",
      "          size (1.8,0.0883587,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-117.9,0)\n",
      "          size (1.8,0.132253,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     block, center = (1.11022e-16,-117.6,0)\n",
      "          size (1.8,0.190581,1e+20)\n",
      "          axes (1,0,0), (0,1,0), (0,0,1)\n",
      "          dielectric constant epsilon diagonal = (2.25,2.25,2.25)\n",
      "     ...(+ 792 objects not shown)...\n",
      "time for set_epsilon = 10.2552 s\n",
      "-----------\n",
      "Meep progress: 2.2800000000000002/125.0 = 1.8% done in 4.0s, 215.4s to go\n",
      "on time step 228 (time=2.28), 0.0175505 s/step\n",
      "Meep progress: 5.0600000000000005/125.0 = 4.0% done in 8.0s, 189.8s to go\n",
      "on time step 506 (time=5.06), 0.0144061 s/step\n",
      "Meep progress: 7.79/125.0 = 6.2% done in 12.0s, 180.8s to go\n",
      "on time step 779 (time=7.79), 0.0146795 s/step\n",
      "Meep progress: 10.61/125.0 = 8.5% done in 16.0s, 172.7s to go\n",
      "on time step 1061 (time=10.61), 0.0142034 s/step\n",
      "Meep progress: 13.370000000000001/125.0 = 10.7% done in 20.0s, 167.2s to go\n",
      "on time step 1337 (time=13.37), 0.0145284 s/step\n",
      "Meep progress: 16.12/125.0 = 12.9% done in 24.0s, 162.4s to go\n",
      "on time step 1612 (time=16.12), 0.014564 s/step\n",
      "Meep progress: 17.88/125.0 = 14.3% done in 28.1s, 168.1s to go\n",
      "on time step 1788 (time=17.88), 0.0228108 s/step\n",
      "Meep progress: 20.5/125.0 = 16.4% done in 32.1s, 163.4s to go\n",
      "on time step 2050 (time=20.5), 0.015286 s/step\n",
      "Meep progress: 23.17/125.0 = 18.5% done in 36.1s, 158.5s to go\n",
      "on time step 2317 (time=23.17), 0.015032 s/step\n",
      "Meep progress: 25.310000000000002/125.0 = 20.2% done in 40.1s, 157.8s to go\n",
      "on time step 2531 (time=25.31), 0.0186941 s/step\n",
      "Meep progress: 28.01/125.0 = 22.4% done in 44.1s, 152.7s to go\n",
      "on time step 2801 (time=28.01), 0.0148529 s/step\n",
      "Meep progress: 30.72/125.0 = 24.6% done in 48.1s, 147.6s to go\n",
      "on time step 3072 (time=30.72), 0.0147973 s/step\n",
      "Meep progress: 33.410000000000004/125.0 = 26.7% done in 52.1s, 142.8s to go\n",
      "on time step 3341 (time=33.41), 0.0148998 s/step\n",
      "Meep progress: 36.11/125.0 = 28.9% done in 56.1s, 138.1s to go\n",
      "on time step 3611 (time=36.11), 0.0148395 s/step\n",
      "Meep progress: 38.31/125.0 = 30.6% done in 60.1s, 136.1s to go\n",
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      "Meep progress: 40.82/125.0 = 32.7% done in 64.1s, 132.3s to go\n",
      "on time step 4082 (time=40.82), 0.0159877 s/step\n",
      "Meep progress: 43.5/125.0 = 34.8% done in 68.2s, 127.7s to go\n",
      "on time step 4350 (time=43.5), 0.0149743 s/step\n",
      "Meep progress: 46.17/125.0 = 36.9% done in 72.2s, 123.2s to go\n",
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      "Meep progress: 48.86/125.0 = 39.1% done in 76.2s, 118.7s to go\n",
      "on time step 4886 (time=48.86), 0.0149015 s/step\n",
      "Meep progress: 51.57/125.0 = 41.3% done in 80.2s, 114.2s to go\n",
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      "Meep progress: 54.26/125.0 = 43.4% done in 84.2s, 109.8s to go\n",
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      "Meep progress: 56.97/125.0 = 45.6% done in 88.2s, 105.3s to go\n",
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      "Meep progress: 59.660000000000004/125.0 = 47.7% done in 92.2s, 101.0s to go\n",
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      "Meep progress: 62.36/125.0 = 49.9% done in 96.2s, 96.7s to go\n",
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      "Meep progress: 65.07000000000001/125.0 = 52.1% done in 100.2s, 92.3s to go\n",
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      "Meep progress: 67.77/125.0 = 54.2% done in 104.3s, 88.0s to go\n",
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      "Meep progress: 69.79/125.0 = 55.8% done in 108.3s, 85.6s to go\n",
      "on time step 6979 (time=69.79), 0.0198233 s/step\n",
      "Meep progress: 72.57000000000001/125.0 = 58.1% done in 112.3s, 81.1s to go\n",
      "on time step 7257 (time=72.57), 0.0144427 s/step\n",
      "Meep progress: 75.16/125.0 = 60.1% done in 116.3s, 77.1s to go\n",
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      "Meep progress: 80.57000000000001/125.0 = 64.5% done in 124.3s, 68.5s to go\n",
      "on time step 8057 (time=80.57), 0.0143637 s/step\n",
      "Meep progress: 83.37/125.0 = 66.7% done in 128.3s, 64.1s to go\n",
      "on time step 8337 (time=83.37), 0.0143218 s/step\n",
      "Meep progress: 86.07000000000001/125.0 = 68.9% done in 132.3s, 59.8s to go\n",
      "on time step 8607 (time=86.07), 0.0148534 s/step\n",
      "Meep progress: 88.82000000000001/125.0 = 71.1% done in 136.3s, 55.5s to go\n",
      "on time step 8882 (time=88.82), 0.0145593 s/step\n",
      "Meep progress: 91.74/125.0 = 73.4% done in 140.3s, 50.9s to go\n",
      "on time step 9174 (time=91.74), 0.0137382 s/step\n",
      "Meep progress: 94.64/125.0 = 75.7% done in 144.3s, 46.3s to go\n",
      "on time step 9464 (time=94.64), 0.0138319 s/step\n",
      "Meep progress: 97.55/125.0 = 78.0% done in 148.3s, 41.7s to go\n",
      "on time step 9755 (time=97.55), 0.0137471 s/step\n",
      "Meep progress: 100.47/125.0 = 80.4% done in 152.4s, 37.2s to go\n",
      "on time step 10047 (time=100.47), 0.0137389 s/step\n",
      "Meep progress: 103.35000000000001/125.0 = 82.7% done in 156.4s, 32.8s to go\n",
      "on time step 10335 (time=103.35), 0.01392 s/step\n",
      "Meep progress: 106.25/125.0 = 85.0% done in 160.4s, 28.3s to go\n",
      "on time step 10625 (time=106.25), 0.0138042 s/step\n",
      "Meep progress: 109.14/125.0 = 87.3% done in 164.4s, 23.9s to go\n",
      "on time step 10914 (time=109.14), 0.0138697 s/step\n",
      "Meep progress: 112.01/125.0 = 89.6% done in 168.4s, 19.5s to go\n",
      "on time step 11201 (time=112.01), 0.0139446 s/step\n",
      "Meep progress: 114.66/125.0 = 91.7% done in 172.4s, 15.5s to go\n",
      "on time step 11466 (time=114.66), 0.01511 s/step\n",
      "Meep progress: 117.28/125.0 = 93.8% done in 176.4s, 11.6s to go\n",
      "on time step 11728 (time=117.28), 0.0153079 s/step\n",
      "Meep progress: 119.9/125.0 = 95.9% done in 180.4s, 7.7s to go\n",
      "on time step 11990 (time=119.9), 0.0152988 s/step\n",
      "Meep progress: 122.79/125.0 = 98.2% done in 184.4s, 3.3s to go\n",
      "on time step 12279 (time=122.79), 0.0138704 s/step\n",
      "run 0 finished at t = 125.0 (12500 timesteps)\n",
      "get_farfields_array working on point 236 of 1000 (23% done), 0.0170414 s/point\n",
      "get_farfields_array working on point 472 of 1000 (47% done), 0.0170053 s/point\n",
      "get_farfields_array working on point 704 of 1000 (70% done), 0.0172601 s/point\n",
      "get_farfields_array working on point 937 of 1000 (93% done), 0.0171973 s/point\n"
     ]
    }
   ],
   "source": [
    "gdc_new = np.linspace(0.16, 0.65, 500)\n",
    "mode_phase_interp = np.interp(gdc_new, gdc, mode_phase)\n",
    "print(\"phase-range:, {:.6f}\".format(mode_phase_interp.max() - mode_phase_interp.min()))\n",
    "\n",
    "phase_tol = 1e-2\n",
    "num_cells = [100, 200, 400]\n",
    "ff_nc = np.empty((spot_length * ff_res, len(num_cells)))\n",
    "\n",
    "for k in range(len(num_cells)):\n",
    "    gdc_list = []\n",
    "    for j in range(-num_cells[k], num_cells[k] + 1):\n",
    "        phase_local = (\n",
    "            2\n",
    "            * np.pi\n",
    "            / lcen\n",
    "            * (focal_length - ((j * gp) ** 2 + focal_length**2) ** 0.5)\n",
    "        )  # local phase at the center of the j'th unit cell\n",
    "        phase_mod = phase_local % (-2 * np.pi)  # restrict phase to [-2*pi,0]\n",
    "        if phase_mod > mode_phase_interp.max():\n",
    "            phase_mod = mode_phase_interp.max()\n",
    "        if phase_mod < mode_phase_interp.min():\n",
    "            phase_mod = mode_phase_interp.min()\n",
    "        idx = np.transpose(\n",
    "            np.nonzero(\n",
    "                np.logical_and(\n",
    "                    mode_phase_interp > phase_mod - phase_tol,\n",
    "                    mode_phase_interp < phase_mod + phase_tol,\n",
    "                )\n",
    "            )\n",
    "        ).ravel()\n",
    "        gdc_list.append(gdc_new[idx[0]])\n",
    "\n",
    "    ff_nc[:, k] = grating(gp, gh, gdc_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shown below is the supercell lens design involving 201 unit cells. Note that even though periodic boundaries are used in the supercell calculation (via the `k_point`), the choice of cell boundaries in the *y* (or longitudinal) direction is *irrelevant* given the finite length of the lens. For example, PMLs could also have been used (at the expense of a larger cell). Although [`add_near2far`](https://meep.readthedocs.io/en/latest/Python_User_Interface/#near-to-far-field-spectra) does support periodic boundaries (via the `nperiods` parameter), it is not necessary for this particular example.\n",
    "\n",
    "![](https://meep.readthedocs.io/en/latest/images/metasurface_lens_epsilon.png)\n",
    "\n",
    "The far-field energy-density profile is shown below for the three lens designs. As the number of unit cells increases, the focal spot becomes sharper and sharper. This is expected since the longer the focal length, the bigger the lens required to demonstrate focusing (which means more unit cells). In this example, the largest lens design contains 801 unit cells which corresponds to 0.24 mm or 1.2X the focal length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(\n",
    "    focal_length - 0.5 * spot_length,\n",
    "    focal_length + 0.5 * spot_length,\n",
    "    ff_res * spot_length,\n",
    ")\n",
    "plt.figure(dpi=200)\n",
    "plt.semilogy(\n",
    "    x, abs(ff_nc[:, 0]) ** 2, \"bo-\", label=\"num_cells = {}\".format(2 * num_cells[0] + 1)\n",
    ")\n",
    "plt.semilogy(\n",
    "    x, abs(ff_nc[:, 1]) ** 2, \"ro-\", label=\"num_cells = {}\".format(2 * num_cells[1] + 1)\n",
    ")\n",
    "plt.semilogy(\n",
    "    x, abs(ff_nc[:, 2]) ** 2, \"go-\", label=\"num_cells = {}\".format(2 * num_cells[2] + 1)\n",
    ")\n",
    "plt.xlabel(\"x coordinate (μm)\")\n",
    "plt.ylabel(r\"energy density of far-field electric fields, |E$_z$|$^2$\")\n",
    "plt.title(\"focusing properties of a binary-grating metasurface lens\")\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
